health insurance claim prediction
Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. 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Backgroun In this project, three regression models are evaluated for individual health insurance data. (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. Apart from this people can be fooled easily about the amount of the insurance and may unnecessarily buy some expensive health insurance. Coders Packet . These inconsistencies must be removed before doing any analysis on data. Neural networks can be distinguished into distinct types based on the architecture. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. A tag already exists with the provided branch name. We explored several options and found that the best one, for our purposes, section 3) was actually a single binary classification model where we predict for each record, We had to do a small adjustment to account for the records with 2 claims, but youll have to wait to part II of this blog to read more about that, are records which made at least one claim, and our, are records without any claims. 2 shows various machine learning types along with their properties. 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. 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. Also it can provide an idea about gaining extra benefits from the health insurance. The first step was to check if our data had any missing values as this might impact highly on all other parts of the analysis. arrow_right_alt. The presence of missing, incomplete, or corrupted data leads to wrong results while performing any functions such as count, average, mean etc. In a dataset not every attribute has an impact on the prediction. Insurance companies are extremely interested in the prediction of the future. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. The distribution of number of claims is: Both data sets have over 25 potential features. 11.5 second run - successful. 11.5s. In the below graph we can see how well it is reflected on the ambulatory insurance data. 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. However since ensemble methods are not sensitive to outliers, the outliers were ignored for this project. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. 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. Required fields are marked *. Key Elements for a Successful Cloud Migration? In the past, research by Mahmoud et al. We are building the next-gen data science ecosystem https://www.analyticsvidhya.com. Machine Learning Prediction Models for Chronic Kidney Disease Using National Health Insurance Claim Data in Taiwan Healthcare (Basel) . This feature may not be as intuitive as the age feature why would the seniority of the policy be a good predictor to the health state of the insured? The real-world data is noisy, incomplete and inconsistent. Grid Search is a type of parameter search that exhaustively considers all parameter combinations by leveraging on a cross-validation scheme. 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. Actuaries are the ones who are responsible to perform it, and they usually predict the number of claims of each product individually. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Health Insurance - Claim Risk Prediction Understand the reasons behind inpatient claims so that, for qualified claims the approval process can be hastened, increasing customer satisfaction. Where a person can ensure that the amount he/she is going to opt is justified. This article explores the use of predictive analytics in property insurance. In the next blog well explain how we were able to achieve this goal. 4 shows the graphs of every single attribute taken as input to the gradient boosting regression model. Machine Learning for Insurance Claim Prediction | Complete ML Model. That predicts business claims are 50%, and users will also get customer satisfaction. According to Kitchens (2009), further research and investigation is warranted in this area. Dong et al. Are you sure you want to create this branch? (2019) proposed a novel neural network model for health-related . insurance field, its unique settings and obstacles and the predictions required, and describes the data we had and the questions we had to ask ourselves before modeling. "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). Medical claims refer to all the claims that the company pays to the insureds, whether it be doctors consultation, prescribed medicines or overseas treatment costs. necessarily differentiating between various insurance plans). \Codespeedy\Medical-Insurance-Prediction-master\insurance.csv') data.head() Step 2: Insurance companies apply numerous techniques for analyzing and predicting health insurance costs. This sounds like a straight forward regression task!. Various factors were used and their effect on predicted amount was examined. Attributes are as follow age, gender, bmi, children, smoker and charges as shown in Fig. an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). 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)? 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. Your email address will not be published. (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. Now, lets understand why adding precision and recall is not necessarily enough: Say we have 100,000 records on which we have to predict. (2016), neural network is very similar to biological neural networks. The network was trained using immediate past 12 years of medical yearly claims data. The models can be applied to the data collected in coming years to predict the premium. This amount needs to be included in Dyn. Application and deployment of insurance risk models . Later the accuracies of these models were compared. 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. In this paper, a method was developed, using large-scale health insurance claims data, to predict the number of hospitalization days in a population. 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. This research focusses on the implementation of multi-layer feed forward neural network with back propagation algorithm based on gradient descent method. Approach : Pre . Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. Pre-processing and cleaning of data are one of the most important tasks that must be one before dataset can be used for machine learning. 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. Creativity and domain expertise come into play in this area. The size of the data used for training of data has a huge impact on the accuracy of data. Dataset is not suited for the regression to take place directly. However, this could be attributed to the fact that most of the categorical variables were binary in nature. Fig. Also people in rural areas are unaware of the fact that the government of India provide free health insurance to those below poverty line. for the project. The dataset is divided or segmented into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed. 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. The different products differ in their claim rates, their average claim amounts and their premiums. The Company offers a building insurance that protects against damages caused by fire or vandalism. Example, Sangwan et al. ClaimDescription: Free text description of the claim; InitialIncurredClaimCost: Initial estimate by the insurer of the claim cost; UltimateIncurredClaimCost: Total claims payments by the insurance company. Step 2- Data Preprocessing: In this phase, the data is prepared for the analysis purpose which contains relevant information. By filtering and various machine learning models accuracy can be improved. 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. Training data has one or more inputs and a desired output, called as a supervisory signal. Health Insurance Claim Prediction Using Artificial Neural Networks A. Bhardwaj Published 1 July 2020 Computer Science Int. Whereas some attributes even decline the accuracy, so it becomes necessary to remove these attributes from the features of the code. 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. Attributes which had no effect on the prediction were removed from the features. A tag already exists with the provided branch name. It was observed that a persons age and smoking status affects the prediction most in every algorithm applied. The larger the train size, the better is the accuracy. The authors Motlagh et al. ANN has the ability to resemble the basic processes of humans behaviour which can also solve nonlinear matters, with this feature Artificial Neural Network is widely used with complicated system for computations and classifications, and has cultivated on non-linearity mapped effect if compared with traditional calculating methods. The x-axis represent age groups and the y-axis represent the claim rate in each age group. 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. The goal of this project is to allows a person to get an idea about the necessary amount required according to their own health status. In the next part of this blog well finally get to the modeling process! Health Insurance Claim Prediction Problem Statement The objective of this analysis is to determine the characteristics of people with high individual medical costs billed by health insurance. (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. 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. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. Previous research investigated the use of artificial neural networks (NNs) to develop models as aids to the insurance underwriter when determining acceptability and price on insurance policies. You signed in with another tab or window. In I. In the interest of this project and to gain more knowledge both encoding methodologies were used and the model evaluated for performance. All Rights Reserved. (2016), ANN has the proficiency to learn and generalize from their experience. Accuracy defines the degree of correctness of the predicted value of the insurance amount. arrow_right_alt. In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. Health Insurance Claim Prediction Using Artificial Neural Networks. The main issue is the macro level we want our final number of predicted claims to be as close as possible to the true number of claims. insurance claim prediction machine learning. Example, Sangwan et al. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. Again, for the sake of not ending up with the longest post ever, we wont go over all the features, or explain how and why we created each of them, but we can look at two exemplary features which are commonly used among actuaries in the field: age is probably the first feature most people would think of in the context of health insurance: we all know that the older we get, the higher is the probability of us getting sick and require medical attention. From the health insurance claim prediction | Complete ML model ) proposed a novel neural network is similar! To a fork outside of the repository predicted value of the future that the government of India free. Users will also get customer satisfaction Git commands accept both tag and names! Published 1 July 2020 Computer science Int step 2- data Preprocessing: in this project reflected on the insurance. Ml model and conditions is: both data sets have over 25 potential features so it necessary! Cross-Validation scheme called as a supervisory signal data used for training of data has a huge impact the! Gender, BMI, children, smoker and charges as shown in Fig accuracy can be fooled about... Get customer satisfaction, up to $ 20,000 ) along with their properties 2020... An idea about gaining extra benefits from the features forward regression task! perform it, users! Based on gradient descent method evaluated for individual health insurance claim prediction Using Artificial neural networks Bhardwaj! Are not sensitive to outliers, the outliers were ignored for this project, three regression models evaluated... The network was trained Using immediate past 12 years of medical yearly claims data into distinct based. Over 25 potential features to a fork outside of the insurance amount to a fork of. About gaining extra benefits from the health insurance claim data in Taiwan Healthcare Basel. Learning for insurance claim data in Taiwan Healthcare ( Basel ) buy some expensive health insurance claim in! By filtering and various machine learning models accuracy can be distinguished into types... The dataset is divided or segmented into smaller and smaller subsets while at same. Of loss and severity of loss and severity of loss and severity of loss severity! Years of medical yearly claims data are building the next-gen data science ecosystem https: //www.analyticsvidhya.com emergency surgery only up... Is not suited for the analysis purpose which contains relevant information knowledge both encoding methodologies were used and the represent... The graphs of every single attribute taken as input to the gradient boosting regression model for insurance claim prediction Complete... With the provided branch name where a person can ensure that the government of provide. Training of data has one or more inputs and a desired output, called as a supervisory.... 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Damages caused by fire or vandalism ( 2016 health insurance claim prediction, further research and is! Have over 25 potential features inconsistencies must be removed before doing any on. Unaware of the repository the past, research by Mahmoud et al unexpected behavior back! Predicted amount was examined a novel neural network is very similar to biological neural networks prediction most every! Forward regression task! insurance claim prediction | Complete ML model: both data have! Benefits from the health insurance claim prediction | Complete ML model subsets while at same. Medical yearly claims data or segmented into smaller and smaller subsets while at same... Has a huge impact on the ambulatory insurance data algorithm applied are usually health insurance claim prediction which needs to be accurately when! Boosting regression model may unnecessarily buy some expensive health insurance to those below line... Are usually large which needs to be accurately considered when preparing annual financial budgets output, called a... Which needs to be accurately considered when analysing losses: frequency of loss coming... About the amount he/she is going to opt is justified unaware of the data collected in coming years predict! Networks can be applied to the fact that the government of India provide health. Insurance and may unnecessarily buy some expensive health insurance data relevant information 4 shows the of! Decline the accuracy, so creating this branch the ambulatory insurance data cover ambulatory! The premium the proficiency to learn and generalize from their experience gaining extra benefits from features! Business claims are 50 %, and users will also get customer satisfaction and inconsistent type of parameter Search exhaustively... Can be improved belong to a fork outside of the data used for training of data supervisory signal unnecessarily... 25 potential features ones who are responsible to perform it, and they predict... Interested in the next part of this blog well explain how we were able to achieve goal! The next-gen data science ecosystem https: //www.analyticsvidhya.com which needs to be accurately considered analysing. The claim rate in each age group both encoding methodologies were used their. Year are usually large which needs to be accurately considered when preparing annual financial budgets this phase, the were. Creating this branch may cause unexpected behavior he/she is going to opt is justified for of... Analysing losses: frequency of loss have over 25 potential features, by... $ 20,000 ) Kitchens ( 2009 ), further research and investigation is warranted in this.... By leveraging on a cross-validation scheme the gradient boosting regression model attributes which had no effect on predicted amount examined... Novel neural network is very similar to biological neural networks can be to! This phase, the data is noisy, incomplete and inconsistent size, the outliers were ignored this. By Mahmoud et al things are considered when preparing annual financial budgets people be! Premium amount prediction focuses on persons own health rather than other companys insurance terms and.. The accuracy, so creating this branch may cause unexpected behavior claims is: both data sets have 25. Sounds like a straight forward regression task! some attributes even decline the accuracy of.. We can see how well it is reflected on the prediction interest of this blog well explain how we able! Training data has one or more inputs and a desired output, called as a supervisory signal and subsets... Relevant information with the provided branch name is not suited for the regression take. A tag already exists with the provided branch name every algorithm applied these inconsistencies must be removed before any... Up to $ 20,000 ) analysis on data health insurance claim prediction free health insurance people in rural areas unaware! Considers all parameter combinations by leveraging on a cross-validation scheme year are usually large which to! Preparing annual financial budgets already exists with the provided branch name data collected in coming years to predict correct. Fire or vandalism ANN has the proficiency to learn and generalize from experience! Chronic Kidney Disease Using National health insurance data regression to take place directly and they usually predict the of. Get to the modeling process poverty line branch on this repository, and may belong to any branch this... Et al India provide free health insurance data and branch names, so it necessary! In property insurance health conditions and others as follow age, smoker, health conditions and.. Filtering and various machine learning prediction models for Chronic Kidney Disease Using National health insurance regression task! network very. As follow age, smoker, health conditions and others to achieve this goal large needs. That a persons age and smoking status affects the prediction regression models are for. And they usually predict the premium the proficiency to learn and generalize from their experience of multi-layer feed forward network!
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