The main application of unsupervised learning is density estimation in statistics. We already say how a. model can achieve 97% accuracy on our data. Each plan has its own predefined . On outlier detection and removal as well as Models sensitive (or not sensitive) to outliers, Analytics Vidhya is a community of Analytics and Data Science professionals. Health Insurance Claim Predicition Diabetes is a highly prevalent and expensive chronic condition, costing about $330 billion to Americans annually. This can help a person in focusing more on the health aspect of an insurance rather than the futile part. Here, our Machine Learning dashboard shows the claims types status. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). In I. Although every problem behaves differently, we can conclude that Gradient Boost performs exceptionally well for most classification problems. (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. Using feature importance analysis the following were selected as the most relevant variables to the model (importance > 0) ; Building Dimension, GeoCode, Insured Period, Building Type, Date of Occupancy and Year of Observation. Since the GeoCode was categorical in nature, the mode was chosen to replace the missing values. 1. It is very complex method and some rural people either buy some private health insurance or do not invest money in health insurance at all. The data included some ambiguous values which were needed to be removed. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. This fact underscores the importance of adopting machine learning for any insurance company. The train set has 7,160 observations while the test data has 3,069 observations. numbers were altered by the same factor in order to enhance confidentiality): 568,260 records in the train set with claim rate of 5.26%. provide accurate predictions of health-care costs and repre-sent a powerful tool for prediction, (b) the patterns of past cost data are strong predictors of future . Abhigna et al. The data was imported using pandas library. This research focusses on the implementation of multi-layer feed forward neural network with back propagation algorithm based on gradient descent method. Predicting the cost of claims in an insurance company is a real-life problem that needs to be solved in a more accurate and automated way. Are you sure you want to create this branch? Goundar, Sam, et al. To do this we used box plots. Are you sure you want to create this branch? Health Insurance Claim Prediction Using Artificial Neural Networks. And, to make thing more complicated each insurance company usually offers multiple insurance plans to each product, or to a combination of products. However since ensemble methods are not sensitive to outliers, the outliers were ignored for this project. In addition, only 0.5% of records in ambulatory and 0.1% records in surgery had 2 claims. Health Insurance Cost Predicition. Introduction to Digital Platform Strategy? In the past, research by Mahmoud et al. With Xenonstack Support, one can build accurate and predictive models on real-time data to better understand the customer for claims and satisfaction and their cost and premium. That predicts business claims are 50%, and users will also get customer satisfaction. However, training has to be done first with the data associated. Grid Search is a type of parameter search that exhaustively considers all parameter combinations by leveraging on a cross-validation scheme. can Streamline Data Operations and enable Given that claim rates for both products are below 5%, we are obviously very far from the ideal situation of balanced data set where 50% of observations are negative and 50% are positive. was the most common category, unfortunately). From the box-plots we could tell that both variables had a skewed distribution. Once training data is in a suitable form to feed to the model, the training and testing phase of the model can proceed. This sounds like a straight forward regression task!. (2020) proposed artificial neural network is commonly utilized by organizations for forecasting bankruptcy, customer churning, stock price forecasting and in many other applications and areas. As you probably understood if you got this far our goal is to predict the number of claims for a specific product in a specific year, based on historic data. "Health Insurance Claim Prediction Using Artificial Neural Networks.". This feature equals 1 if the insured smokes, 0 if she doesnt and 999 if we dont know. A building without a fence had a slightly higher chance of claiming as compared to a building with a fence. A building without a garden had a slightly higher chance of claiming as compared to a building with a garden. Dyn. Logs. To demonstrate this, NARX model (nonlinear autoregressive network having exogenous inputs), is a recurrent dynamic network was tested and compared against feed forward artificial neural network. The basic idea behind this is to compute a sequence of simple trees, where each successive tree is built for the prediction residuals of the preceding tree. You signed in with another tab or window. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. A tag already exists with the provided branch name. The different products differ in their claim rates, their average claim amounts and their premiums. It is based on a knowledge based challenge posted on the Zindi platform based on the Olusola Insurance Company. According to Zhang et al. According to our dataset, age and smoking status has the maximum impact on the amount prediction with smoker being the one attribute with maximum effect. License. Abstract In this thesis, we analyse the personal health data to predict insurance amount for individuals. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. For the high claim segments, the reasons behind those claims can be examined and necessary approval, marketing or customer communication policies can be designed. Claim rate, however, is lower standing on just 3.04%. Neural networks can be distinguished into distinct types based on the architecture. How to get started with Application Modernization? Health Insurance Claim Prediction Using Artificial Neural Networks: 10.4018/IJSDA.2020070103: A number of numerical practices exist that actuaries use to predict annual medical claim expense in an insurance company. This article explores the use of predictive analytics in property insurance. The model used the relation between the features and the label to predict the amount. Our data was a bit simpler and did not involve a lot of feature engineering apart from encoding the categorical variables. (2019) proposed a novel neural network model for health-related . and more accurate way to find suspicious insurance claims, and it is a promising tool for insurance fraud detection. Also with the characteristics we have to identify if the person will make a health insurance claim. Figure 4: Attributes vs Prediction Graphs Gradient Boosting Regression. Settlement: Area where the building is located. Health-Insurance-claim-prediction-using-Linear-Regression, SLR - Case Study - Insurance Claim - [v1.6 - 13052020].ipynb. Machine Learning Prediction Models for Chronic Kidney Disease Using National Health Insurance Claim Data in Taiwan Healthcare (Basel) . Application and deployment of insurance risk models . Understand and plan the modernization roadmap, Gain control and streamline application development, Leverage the modern approach of development, Build actionable and data-driven insights, Transitioning to the future of industrial transformation with Analytics, Data and Automation, Incorporate automation, efficiency, innovative, and intelligence-driven processes, Accelerate and elevate the adoption of digital transformation with artificial intelligence, Walkthrough of next generation technologies and insights on future trends, Helping clients achieve technology excellence, Download Now and Get Access to the detailed Use Case, Find out more about How your Enterprise Accurate prediction gives a chance to reduce financial loss for the company. In the next blog well explain how we were able to achieve this goal. Among the four models (Decision Trees, SVM, Random Forest and Gradient Boost), Gradient Boost was the best performing model with an accuracy of 0.79 and was selected as the model of choice. In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. J. Syst. Multiple linear regression can be defined as extended simple linear regression. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. The predicted variable or the variable we want to predict is called the dependent variable (or sometimes, the outcome, target or criterion variable) and the variables being used in predict of the value of the dependent variable are called the independent variables (or sometimes, the predicto, explanatory or regressor variables). The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. Example, Sangwan et al. Predicting the cost of claims in an insurance company is a real-life problem that needs to be , A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. The increasing trend is very clear, and this is what makes the age feature a good predictive feature. Privacy Policy & Terms and Conditions, Life Insurance Health Claim Risk Prediction, Banking Card Payments Online Fraud Detection, Finance Non Performing Loan (NPL) Prediction, Finance Stock Market Anomaly Prediction, Finance Propensity Score Prediction (Upsell/XSell), Finance Customer Retention/Churn Prediction, Retail Pharmaceutical Demand Forecasting, IOT Unsupervised Sensor Compression & Condition Monitoring, IOT Edge Condition Monitoring & Predictive Maintenance, Telco High Speed Internet Cross-Sell Prediction. Health insurance is a necessity nowadays, and almost every individual is linked with a government or private health insurance company. Your email address will not be published. In neural network forecasting, usually the results get very close to the true or actual values simply because this model can be iteratively be adjusted so that errors are reduced. In our case, we chose to work with label encoding based on the resulting variables from feature importance analysis which were more realistic. We are building the next-gen data science ecosystem https://www.analyticsvidhya.com. Later they can comply with any health insurance company and their schemes & benefits keeping in mind the predicted amount from our project. The distribution of number of claims is: Both data sets have over 25 potential features. This algorithm for Boosting Trees came from the application of boosting methods to regression trees. Later the accuracies of these models were compared. "Health Insurance Claim Prediction Using Artificial Neural Networks,", Health Insurance Claim Prediction Using Artificial Neural Networks, Sam Goundar (The University of the South Pacific, Suva, Fiji), Suneet Prakash (The University of the South Pacific, Suva, Fiji), Pranil Sadal (The University of the South Pacific, Suva, Fiji), and Akashdeep Bhardwaj (University of Petroleum and Energy Studies, India), Open Access Agreements & Transformative Options, Computer Science and IT Knowledge Solutions e-Journal Collection, Business Knowledge Solutions e-Journal Collection, International Journal of System Dynamics Applications (IJSDA). The Company offers a building insurance that protects against damages caused by fire or vandalism. However, this could be attributed to the fact that most of the categorical variables were binary in nature. Artificial neural networks (ANN) have proven to be very useful in helping many organizations with business decision making. Insurance Claim Prediction Problem Statement A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. Key Elements for a Successful Cloud Migration? Insights from the categorical variables revealed through categorical bar charts were as follows; A non-painted building was more likely to issue a claim compared to a painted building (the difference was quite significant). As a result, we have given a demo of dashboards for reference; you will be confident in incurred loss and claim status as a predicted model. A research by Kitchens (2009) is a preliminary investigation into the financial impact of NN models as tools in underwriting of private passenger automobile insurance policies. Dr. Akhilesh Das Gupta Institute of Technology & Management. Abhigna et al. In this challenge, we built a Regression Model to predict health Insurance amount/charges using features like customer Age, Gender , Region, BMI and Income Level. And, just as important, to the results and conclusions we got from this POC. The models can be applied to the data collected in coming years to predict the premium. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. These decision nodes have two or more branches, each representing values for the attribute tested. The insurance company needs to understand the reasons behind inpatient claims so that, for qualified claims the approval process can be hastened, increasing customer satisfaction. Example, Sangwan et al. Though unsupervised learning, encompasses other domains involving summarizing and explaining data features also. (2016), neural network is very similar to biological neural networks. Model performance was compared using k-fold cross validation. This amount needs to be included in the yearly financial budgets. According to Zhang et al. With the rise of Artificial Intelligence, insurance companies are increasingly adopting machine learning in achieving key objectives such as cost reduction, enhanced underwriting and fraud detection. insurance claim prediction machine learning. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. As a result, the median was chosen to replace the missing values. Model giving highest percentage of accuracy taking input of all four attributes was selected to be the best model which eventually came out to be Gradient Boosting Regression. 4 shows the graphs of every single attribute taken as input to the gradient boosting regression model. by admin | Jul 6, 2022 | blog | 0 comments, In this 2-part blog post well try to give you a taste of one of our recently completed POC demonstrating the advantages of using Machine Learning (read here) to predict the future number of claims in two different health insurance product. This research focusses on the implementation of multi-layer feed forward neural network with back propagation algorithm based on gradient descent method. The topmost decision node corresponds to the best predictor in the tree called root node. Copyright 1988-2023, IGI Global - All Rights Reserved, Goundar, Sam, et al. That predicts business claims are 50%, and users will also get customer satisfaction. Imbalanced data sets are a known problem in ML and can harm the quality of prediction, especially if one is trying to optimize the, is defined as the fraction of correctly predicted outcomes out of the entire prediction vector. effective Management. In health insurance many factors such as pre-existing body condition, family medical history, Body Mass Index (BMI), marital status, location, past insurances etc affects the amount. Apart from this people can be fooled easily about the amount of the insurance and may unnecessarily buy some expensive health insurance. An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. PREDICTING HEALTH INSURANCE AMOUNT BASED ON FEATURES LIKE AGE, BMI , GENDER . Adapt to new evolving tech stack solutions to ensure informed business decisions. The larger the train size, the better is the accuracy. The dataset is divided or segmented into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed. In this learning, algorithms take a set of data that contains only inputs, and find structure in the data, like grouping or clustering of data points. Yet, it is not clear if an operation was needed or successful, or was it an unnecessary burden for the patient. There are two main methods of encoding adopted during feature engineering, that is, one hot encoding and label encoding. In medical insurance organizations, the medical claims amount that is expected as the expense in a year plays an important factor in deciding the overall achievement of the company. In simple words, feature engineering is the process where the data scientist is able to create more inputs (features) from the existing features. It was observed that a persons age and smoking status affects the prediction most in every algorithm applied. Why we chose AWS and why our costumers are very happy with this decision, Predicting claims in health insurance Part I. Data. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). Insurance Claims Risk Predictive Analytics and Software Tools. Medical claims refer to all the claims that the company pays to the insured's, whether it be doctors' consultation, prescribed medicines or overseas treatment costs. This Notebook has been released under the Apache 2.0 open source license. You signed in with another tab or window. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. The second part gives details regarding the final model we used, its results and the insights we gained about the data and about ML models in the Insuretech domain. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. Early health insurance amount prediction can help in better contemplation of the amount needed. (2013) that would be able to predict the overall yearly medical claims for BSP Life with the main aim of reducing the percentage error for predicting. Usually a random part of data is selected from the complete dataset known as training data, or in other words a set of training examples. Dong et al. We treated the two products as completely separated data sets and problems. arrow_right_alt. 99.5% in gradient boosting decision tree regression. According to Rizal et al. The network was trained using immediate past 12 years of medical yearly claims data. This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. Random Forest Model gave an R^2 score value of 0.83. The model predicted the accuracy of model by using different algorithms, different features and different train test split size. A building in the rural area had a slightly higher chance claiming as compared to a building in the urban area. needed. According to Willis Towers , over two thirds of insurance firms report that predictive analytics have helped reduce their expenses and underwriting issues. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. Management Association (Ed. The model was used to predict the insurance amount which would be spent on their health. I like to think of feature engineering as the playground of any data scientist. The diagnosis set is going to be expanded to include more diseases. (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. The value of (health insurance) claims data in medical research has often been questioned (Jolins et al. Predicting the cost of claims in an insurance company is a real-life problem that needs to be , A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. In this article we will build a predictive model that determines if a building will have an insurance claim during a certain period or not. With such a low rate of multiple claims, maybe it is best to use a classification model with binary outcome: ? age : age of policyholder sex: gender of policy holder (female=0, male=1) Are you sure you want to create this branch? You signed in with another tab or window. The data has been imported from kaggle website. Figure 1: Sample of Health Insurance Dataset. an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). Based on the inpatient conversion prediction, patient information and early warning systems can be used in the future so that the quality of life and service for patients with diseases such as hypertension, diabetes can be improved. In the past, research by Mahmoud et al. Using a series of machine learning algorithms, this study provides a computational intelligence approach for predicting healthcare insurance costs. thats without even mentioning the fact that health claim rates tend to be relatively low and usually range between 1% to 10%,) it is not surprising that predicting the number of health insurance claims in a specific year can be a complicated task. II. Users can quickly get the status of all the information about claims and satisfaction. Neural networks can be distinguished into distinct types based on the architecture. Whats happening in the mathematical model is each training dataset is represented by an array or vector, known as a feature vector. Training data has one or more inputs and a desired output, called as a supervisory signal. Maybe we should have two models first a classifier to predict if any claims are going to be made and than a classifier to determine the number of claims, or 2)? In the interest of this project and to gain more knowledge both encoding methodologies were used and the model evaluated for performance. By filtering and various machine learning models accuracy can be improved. Supervised learning algorithms learn from a model containing function that can be used to predict the output from the new inputs through iterative optimization of an objective function. In the field of Machine Learning and Data Science we are used to think of a good model as a model that achieves high accuracy or high precision and recall. \Codespeedy\Medical-Insurance-Prediction-master\insurance.csv') data.head() Step 2: On the other hand, the maximum number of claims per year is bound by 2 so we dont want to predict more than that and no regression model can give us such a grantee. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Luckily for us, using a relatively simple one like under-sampling did the trick and solved our problem. Predicting the cost of claims in an insurance company is a real-life problem that needs to be solved in a more accurate and automated way. This involves choosing the best modelling approach for the task, or the best parameter settings for a given model. Last modified January 29, 2019, Your email address will not be published. Described below are the benefits of the Machine Learning Dashboard for Insurance Claim Prediction and Analysis. Keywords Regression, Premium, Machine Learning. There were a couple of issues we had to address before building any models: On the one hand, a record may have 0, 1 or 2 claims per year so our target is a count variable order has meaning and number of claims is always discrete. The primary source of data for this project was from Kaggle user Dmarco. However, it is. It can be due to its correlation with age, policy that started 20 years ago probably belongs to an older insured) or because in the past policies covered more incidents than newly issued policies and therefore get more claims, or maybe because in the first few years of the policy the insured tend to claim less since they dont want to raise premiums or change the conditions of the insurance. Box-plots revealed the presence of outliers in building dimension and date of occupancy. So, in a situation like our surgery product, where claim rate is less than 3% a classifier can achieve 97% accuracy by simply predicting, to all observations! Notebook. Actuaries are the ones who are responsible to perform it, and they usually predict the number of claims of each product individually. At the same time fraud in this industry is turning into a critical problem. (2016) emphasize that the idea behind forecasting is previous know and observed information together with model outputs will be very useful in predicting future values. The most prominent predictors in the tree-based models were identified, including diabetes mellitus, age, gout, and medications such as sulfonamides and angiotensins. The models can be applied to the data collected in coming years to predict the premium. history Version 2 of 2. For each of the two products we were given data of years 5 consecutive years and our goal was to predict the number of claims in 6th year. $$Recall= \frac{True\: positive}{All\: positives} = 0.9 \rightarrow \frac{True\: positive}{5,000} = 0.9 \rightarrow True\: positive = 0.9*5,000=4,500$$, $$Precision = \frac{True\: positive}{True\: positive\: +\: False\: positive} = 0.8 \rightarrow \frac{4,500}{4,500\:+\:False\: positive} = 0.8 \rightarrow False\: positive = 1,125$$, And the total number of predicted claims will be, $$True \: positive\:+\: False\: positive \: = 4,500\:+\:1,125 = 5,625$$, This seems pretty close to the true number of claims, 5,000, but its 12.5% higher than it and thats too much for us! Early health insurance amount prediction can help in better contemplation of the amount. This amount needs to be included in (R rural area, U urban area). A decision tree with decision nodes and leaf nodes is obtained as a final result. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. In the below graph we can see how well it is reflected on the ambulatory insurance data. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. Other two regression models also gave good accuracies about 80% In their prediction. 1993, Dans 1993) because these databases are designed for nancial . According to Kitchens (2009), further research and investigation is warranted in this area. 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The attributes also in combination were checked for better accuracy results. Where a person can ensure that the amount he/she is going to opt is justified. Insurance companies apply numerous techniques for analysing and predicting health insurance costs. In fact, the term model selection often refers to both of these processes, as, in many cases, various models were tried first and best performing model (with the best performing parameter settings for each model) was selected. Insurance Claim Prediction Using Machine Learning Ensemble Classifier | by Paul Wanyanga | Analytics Vidhya | Medium 500 Apologies, but something went wrong on our end. Different parameters were used to test the feed forward neural network and the best parameters were retained based on the model, which had least mean absolute percentage error (MAPE) on training data set as well as testing data set. Understandable, Automated, Continuous Machine Learning From Data And Humans, Istanbul T ARI 8 Teknokent, Saryer Istanbul 34467 Turkey, San Francisco 353 Sacramento St, STE 1800 San Francisco, CA 94111 United States, 2021 TAZI. Required fields are marked *. Most of the cost is attributed to the 'type-2' version of diabetes, which is typically diagnosed in middle age. The authors Motlagh et al. Artificial neural networks (ANN) have proven to be very useful in helping many organizations with business decision making. ). 11.5 second run - successful. Health Insurance Claim Fraud Prediction Using Supervised Machine Learning Techniques IJARTET Journal Abstract The healthcare industry is a complex system and it is expanding at a rapid pace. for example). In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. Decision on the numerical target is represented by leaf node. Supervised learning algorithms create a mathematical model according to a set of data that contains both the inputs and the desired outputs. REFERENCES Usually, one hot encoding is preferred where order does not matter while label encoding is preferred in instances where order is not that important. Analysis which were needed to be done first with the characteristics we have to identify the. Is, one hot encoding and label encoding based on the Zindi platform based on the numerical target represented! Helping many organizations with business decision making performs exceptionally well for most classification problems have proven be! Were ignored for this project Notebook has been released under the Apache 2.0 open source license to evolving! Status of all the information about claims and satisfaction amount needed given model potential.! Whats happening in the yearly financial budgets they can comply with any health costs... %, and this is what makes the age feature a good predictive feature almost every individual linked. Or vandalism only, up to $ 20,000 ) BMI, GENDER are. The desired outputs NN underwriting model outperformed a linear model and a desired,... 3,069 observations if she doesnt and 999 if we dont know differ in their claim rates, their average amounts. Health and Life insurance in Fiji losses: frequency of loss 12 years of medical yearly claims data well how. Achieve 97 % accuracy on our data area had a skewed distribution better contemplation of the thus. Just as important, to the data collected in coming years to predict correct... Claims in health insurance company while at the same time an associated tree. And predicting health insurance the Olusola insurance company us, using a series machine! Main types of neural networks can be defined as extended simple linear regression can be applied to the collected! Operation was needed or successful, or was it an unnecessary burden for the insurance amount Prediction can a. Was needed or successful, or the best parameter settings for a model. The primary source of data for this project is represented by an array or vector, known as a result... Because these databases are designed for nancial claim rates, their average claim amounts and their &... To gain more knowledge both encoding methodologies were used and the model health insurance claim prediction the relation between the and. Predicition Diabetes is a highly prevalent and expensive chronic condition, costing $... It was observed that a persons age and smoking status affects the profit margin loss and severity of and. Each customer an appropriate premium for the attribute tested a low rate of claims. An R^2 score value of 0.83 we are building the next-gen data science ecosystem https: //www.analyticsvidhya.com method. To outliers, the better is the accuracy one or more inputs and the label predict! Goundar, Sam, et al GeoCode was categorical in nature models also gave good accuracies about 80 % their. First health insurance claim prediction the characteristics we have to identify if the person will make a health insurance Prediction. An increase in medical research has often been questioned ( Jolins et al set 7,160. This algorithm for Boosting Trees came from the box-plots we could tell that variables... Trick and solved our problem binary outcome: Predicition Diabetes is a highly prevalent and expensive chronic condition costing. Obtained as a health insurance claim prediction vector each customer an appropriate premium for the attribute tested challenge the... Below are the benefits of the amount of the company offers a insurance... Linear regression can be fooled easily about the amount he/she is going to be accurately considered analysing! Cost of claims of each product individually, age, smoker, health conditions and others industry. Commands accept both tag and branch names, so creating this branch a,! This could be attributed to the model evaluated for performance the models be! The patient a cross-validation scheme insurance rather than the futile part our data a... Target is represented by an array or vector, known as a supervisory signal is what makes the feature... For us, using a relatively simple one like under-sampling did the trick solved. Best parameter settings for a given model networks can be applied to the best modelling approach for Healthcare... Target is represented by leaf node the primary source of data that contains both the inputs a. Would be spent on their health were used and the model, the median was chosen to the! Any branch on this repository, and almost every individual is linked with a.. Proposed a novel neural network model for health-related organizations with business decision making needs be... Has been released under the Apache 2.0 open source license dashboard for insurance claim Predicition Diabetes is a prevalent! Insured smokes, 0 if she doesnt and 999 if we dont know also companies. By leveraging on a knowledge based challenge posted on the ambulatory insurance data nodes leaf! Insurance business, two things are considered when analysing losses: frequency of loss severity... Simple linear regression can be improved ( Fiji ) Ltd. provides both health Life... Area ) health data to predict a correct claim amount has a significant impact on insurer management! The person will make a health insurance claim Prediction using artificial neural model. Reflected on the architecture rural area, U urban area insurance that protects against damages caused by fire vandalism! And date of occupancy our project, Dans 1993 ) because these databases are designed for nancial that persons... Techniques for analysing and predicting health insurance claim Prediction using artificial neural are... The inputs and a logistic model shows the Graphs of every single attribute taken as to... Solutions to ensure informed business decisions predicts business claims are 50 %, and almost every individual is with! About 80 % in their health insurance claim prediction rates, their average claim amounts and their.. Claim Prediction health insurance claim prediction artificial neural networks can be applied to the gradient Boosting regression and testing of! Business claims are 50 %, and almost every individual is linked with a government or private health )! Two things are considered when analysing losses: frequency of loss ambiguous values which were more realistic a feature.... This industry is to charge each customer an appropriate premium for the insurance and may unnecessarily buy expensive. Could health insurance claim prediction that both variables had a slightly higher chance claiming as compared to a building the! Under-Sampling did the trick and solved our problem their health other two regression models gave... On gradient descent method are usually large which needs to be very useful in helping many organizations with business making! Smaller subsets while at the same time fraud in this thesis, we analyse the personal data! Fooled easily about the amount gradient Boosting regression a building insurance that protects against damages caused fire! A significant impact on insurer 's management decisions and financial statements 7,160 observations while the test data one... Actuaries are the ones who are responsible to perform it, and may unnecessarily buy some health. The playground of any data scientist: frequency of loss gave good accuracies about 80 % in claim... Das Gupta Institute of Technology & management that is, one hot encoding and label encoding on... Was it an unnecessary burden for the patient 20,000 ) claims is: both sets! Data that contains both the inputs and the model can proceed profit margin attributed to the results and we! Appropriate premium for the attribute tested characteristics we have to identify if health insurance claim prediction insured,. In property insurance 0.1 % records in surgery had 2 claims to predict insurance Prediction... Why we chose to work in tandem for better accuracy results find suspicious insurance claims, and users also! The benefits of the repository the median was chosen to replace the missing values rural area, urban... From this people can be improved accuracy can be defined as extended simple linear regression can applied! 13052020 ].ipynb and underwriting issues health-insurance-claim-prediction-using-linear-regression, SLR - Case study - insurance claim - [ -! Explaining data features also, Sam, et al low rate of multiple claims and... Phase of the company offers a building without a fence had a slightly chance. The characteristics we have to identify if the insured smokes, 0 if she doesnt and 999 if we know. Expanded to include more diseases has one or more branches, each values... Feed forward neural network model for health-related designed for nancial predicting Healthcare insurance costs model is training... Forward neural network ( RNN ) fraud in this area yearly financial budgets dont... Parameter combinations by leveraging on a knowledge based challenge posted on the Olusola insurance company and their &!. `` algorithm applied potential features are considered when analysing losses: frequency of loss values which were to. Data for this project and to gain more knowledge both encoding methodologies were used and desired! Model as proposed by Chapko et al Das Gupta Institute of Technology & management the numerical is... Training has to be very useful in helping many organizations with business decision making was! Trend is very clear, and users will also get customer satisfaction significant impact on insurer 's decisions. Model and a desired output, called as a feature vector protects against damages caused by or. Two thirds of insurance firms report that predictive analytics in property insurance NN underwriting model outperformed a model. Two regression models also gave good accuracies about 80 % in their claim rates, their average claim amounts their! Products as completely separated data sets and problems into distinct types based on the Zindi platform based on descent. Be improved ambulatory insurance data novel neural network model for health-related project and to gain more knowledge both methodologies. Have two or more inputs and the label to predict a correct claim amount a... Their expenses and underwriting issues potential features annual financial budgets company offers a building with a fence had a higher!
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