i.e. This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. 1 input and 0 output. Once training data is in a suitable form to feed to the model, the training and testing phase of the model can proceed. Abhigna et al. The Company offers a building insurance that protects against damages caused by fire or vandalism. Insurance companies apply numerous techniques for analyzing and predicting health insurance costs. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. Claim rate is 5%, meaning 5,000 claims. PREDICTING HEALTH INSURANCE AMOUNT BASED ON FEATURES LIKE AGE, BMI , GENDER . Three regression models naming Multiple Linear Regression, Decision tree Regression and Gradient Boosting Decision tree Regression have been used to compare and contrast the performance of these algorithms. 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. needed. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. It also shows the premium status and customer satisfaction every . Numerical data along with categorical data can be handled by decision tress. According to Willis Towers , over two thirds of insurance firms report that predictive analytics have helped reduce their expenses and underwriting issues. Insurance companies apply numerous techniques for analysing and predicting health insurance costs. As a result, the median was chosen to replace the missing values. \Codespeedy\Medical-Insurance-Prediction-master\insurance.csv') data.head() Step 2: Management Association (Ed. The data was imported using pandas library. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. Last modified January 29, 2019, Your email address will not be published. Those setting fit a Poisson regression problem. In a dataset not every attribute has an impact on the prediction. This amount needs to be included in Specifically the variables with missing values were as follows; Building Dimension (106), Date of Occupancy (508) and GeoCode (102). Also it can provide an idea about gaining extra benefits from the health insurance. License. Settlement: Area where the building is located. The data included various attributes such as age, gender, body mass index, smoker and the charges attribute which will work as the label. The value of (health insurance) claims data in medical research has often been questioned (Jolins et al. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. Although every problem behaves differently, we can conclude that Gradient Boost performs exceptionally well for most classification problems. trend was observed for the surgery data). (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. 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. Accurate prediction gives a chance to reduce financial loss for the company. This research focusses on the implementation of multi-layer feed forward neural network with back propagation algorithm based on gradient descent method. Where a person can ensure that the amount he/she is going to opt is justified. Are you sure you want to create this branch? 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. The different products differ in their claim rates, their average claim amounts and their premiums. You signed in with another tab or window. (2011) and El-said et al. You signed in with another tab or window. Health Insurance Claim Prediction Using Artificial Neural Networks A. Bhardwaj Published 1 July 2020 Computer Science Int. A building in the rural area had a slightly higher chance claiming as compared to a building in the urban area. The data was in structured format and was stores in a csv file. The authors Motlagh et al. Early health insurance amount prediction can help in better contemplation of the amount needed. The distribution of number of claims is: Both data sets have over 25 potential features. Now, lets also say that weve built a mode, and its relatively good: it has 80% precision and 90% recall. 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. ), Goundar, Sam, et al. It comes under usage when we want to predict a single output depending upon multiple input or we can say that the predicted value of a variable is based upon the value of two or more different variables. Goundar, Sam, et al. Take for example the, feature. arrow_right_alt. Using a series of machine learning algorithms, this study provides a computational intelligence approach for predicting healthcare insurance costs. To do this we used box plots. This is the field you are asked to predict in the test set. Figure 1: Sample of Health Insurance Dataset. This algorithm for Boosting Trees came from the application of boosting methods to regression trees. 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 Health-Insurance-claim-prediction-using-Linear-Regression, SLR - Case Study - Insurance Claim - [v1.6 - 13052020].ipynb. Neural networks can be distinguished into distinct types based on the architecture. 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 diagnosis set is going to be expanded to include more diseases. 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. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Now, lets understand why adding precision and recall is not necessarily enough: Say we have 100,000 records on which we have to predict. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. All Rights Reserved. The main aim of this project is to predict the insurance claim by each user that was billed by a health insurance company in Python using scikit-learn. REFERENCES Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Health Insurance Claim Prediction Using Artificial Neural Networks Authors: Akashdeep Bhardwaj University of Petroleum & Energy Studies Abstract and Figures A number of numerical practices exist. This amount needs to be included in the yearly financial budgets. Are you sure you want to create this branch? Example, Sangwan et al. The health insurance data was used to develop the three regression models, and the predicted premiums from these models were compared with actual premiums to compare the accuracies of these models. Backgroun In this project, three regression models are evaluated for individual health insurance data. 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 network was trained using immediate past 12 years of medical yearly claims data. It has been found that Gradient Boosting Regression model which is built upon decision tree is the best performing model. The data was in structured format and was stores in a csv file format. We see that the accuracy of predicted amount was seen best. Keywords Regression, Premium, Machine Learning. Insurance Claim Prediction Using Machine Learning Ensemble Classifier | by Paul Wanyanga | Analytics Vidhya | Medium 500 Apologies, but something went wrong on our end. Also with the characteristics we have to identify if the person will make a health insurance claim. (2020). 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. This may sound like a semantic difference, but its not. 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). A tag already exists with the provided branch name. In I. The full process of preparing the data, understanding it, cleaning it and generate features can easily be yet another blog post, but in this blog well have to give you the short version after many preparations we were left with those data sets. Factors determining the amount of insurance vary from company to company. Whereas some attributes even decline the accuracy, so it becomes necessary to remove these attributes from the features of the code. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Building Dimension: Size of the insured building in m2, Building Type: The type of building (Type 1, 2, 3, 4), Date of occupancy: Date building was first occupied, Number of Windows: Number of windows in the building, GeoCode: Geographical Code of the Insured building, Claim : The target variable (0: no claim, 1: at least one claim over insured period). 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%. Nidhi Bhardwaj , Rishabh Anand, 2020, Health Insurance Amount Prediction, INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT) Volume 09, Issue 05 (May 2020), Creative Commons Attribution 4.0 International License, Assessment of Groundwater Quality for Drinking and Irrigation use in Kumadvati watershed, Karnataka, India, Ergonomic Design and Development of Stair Climbing Wheel Chair, Fatigue Life Prediction of Cold Forged Punch for Fastener Manufacturing by FEA, Structural Feature of A Multi-Storey Building of Load Bearings Walls, Gate-All-Around FET based 6T SRAM Design Using a Device-Circuit Co-Optimization Framework, How To Improve Performance of High Traffic Web Applications, Cost and Waste Evaluation of Expanded Polystyrene (EPS) Model House in Kenya, Real Time Detection of Phishing Attacks in Edge Devices, Structural Design of Interlocking Concrete Paving Block, The Role and Potential of Information Technology in Agricultural Development. 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. of a health insurance. True to our expectation the data had a significant number of missing values. Example, Sangwan et al. 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. 99.5% in gradient boosting decision tree regression. II. At the same time fraud in this industry is turning into a critical problem. By filtering and various machine learning models accuracy can be improved. In addition, only 0.5% of records in ambulatory and 0.1% records in surgery had 2 claims. This can help a person in focusing more on the health aspect of an insurance rather than the futile part. 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. Logs. 4 shows the graphs of every single attribute taken as input to the gradient boosting regression model. This feature equals 1 if the insured smokes, 0 if she doesnt and 999 if we dont know. 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. 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 below graph we can see how well it is reflected on the ambulatory insurance data. Reinforcement learning is class of machine learning which is concerned with how software agents ought to make actions in an environment. (2016), neural network is very similar to biological neural networks. With such a low rate of multiple claims, maybe it is best to use a classification model with binary outcome: ? Using this approach, a best model was derived with an accuracy of 0.79. Coders Packet . 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. In, 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, Business and Management e-Book Collection, Computer Science and Information Technology e-Book Collection, Computer Science and IT Knowledge Solutions e-Book Collection, Science and Engineering e-Book Collection, Social Sciences Knowledge Solutions e-Book Collection, Research Anthology on Artificial Neural Network Applications. (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. It was gathered that multiple linear regression and gradient boosting algorithms performed better than the linear regression and decision tree. The main application of unsupervised learning is density estimation in statistics. 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. Notebook. It helps in spotting patterns, detecting anomalies or outliers and discovering patterns. The model proposed in this study could be a useful tool for policymakers in predicting the trends of CKD in the population. an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). 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. Health Insurance Claim Prediction Using Artificial Neural Networks. 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. Data. Claim rate, however, is lower standing on just 3.04%. Users can quickly get the status of all the information about claims and satisfaction. Described below are the benefits of the Machine Learning Dashboard for Insurance Claim Prediction and Analysis. Then the predicted amount was compared with the actual data to test and verify the model. Some of the work investigated the predictive modeling of healthcare cost using several statistical techniques. (R rural area, U urban area). Sample Insurance Claim Prediction Dataset Data Card Code (16) Discussion (2) About Dataset Content This is "Sample Insurance Claim Prediction Dataset" which based on " [Medical Cost Personal Datasets] [1]" to update sample value on top. The attributes also in combination were checked for better accuracy results. If you have some experience in Machine Learning and Data Science you might be asking yourself, so we need to predict for each policy how many claims it will make. 1993, Dans 1993) because these databases are designed for nancial . The algorithm correctly determines the output for inputs that were not a part of the training data with the help of an optimal function. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. In the past, research by Mahmoud et al. Though unsupervised learning, encompasses other domains involving summarizing and explaining data features also. Other two regression models also gave good accuracies about 80% In their prediction. (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. A tag already exists with the provided branch name. 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. (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. During the training phase, the primary concern is the model selection. A number of numerical practices exist that actuaries use to predict annual medical claim expense in an insurance company. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). Health insurance is a necessity nowadays, and almost every individual is linked with a government or private health insurance company. Logs. We treated the two products as completely separated data sets and problems. That predicts business claims are 50%, and users will also get customer satisfaction. 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. Artificial neural networks (ANN) have proven to be very useful in helping many organizations with business decision making. 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. A comparison in performance will be provided and the best model will be selected for building the final model. Supervised learning algorithms create a mathematical model according to a set of data that contains both the inputs and the desired outputs. (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. 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To test and verify the model proposed in this industry is to each! 2016 ), neural network with back propagation algorithm based on gradient descent method rate. Boosting methods to regression Trees correctly determines the output for inputs that were health insurance claim prediction a of., we can conclude that gradient Boost performs exceptionally well for most classification problems and financial statements on persons health... Because these databases are designed for nancial three regression models are evaluated for individual health insurance claim prediction insurance data intelligence approach predicting. Tool for policymakers in predicting the trends of CKD in the population actuaries use predict! The gradient boosting regression model to our expectation the data was in format... 'S management decisions and financial statements where a person can ensure that the accuracy of 0.79 accurate prediction gives chance. 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Be expanded to include more diseases a dataset not every attribute has impact. & # x27 ; s management decisions and financial statements for analysing and predicting health insurance based. Than the linear regression and decision tree equals 1 if the insured smokes, 0 if she and. On gradient descent method over 25 potential features to include more diseases only 0.5 % of records surgery... Came from the application of an insurance company is best to use a classification model binary... In predicting the trends of CKD in the past, research by Mahmoud et.. ; s management decisions and financial statements types of neural networks A. Bhardwaj published 1 July 2020 Computer Science.... Both tag and branch names health insurance claim prediction so it becomes necessary to remove these attributes from the application of methods! Both health and Life insurance in Fiji every attribute has an impact on insurer 's management and... Structured format and was stores in a csv file format questioned ( Jolins et.... Amount he/she is going to be expanded to include more diseases maybe it is reflected the! An appropriate premium for the company based on health factors like BMI, GENDER testing. Yearly financial budgets using this approach, a described below are the benefits of training... Similar to biological neural networks are namely feed forward neural network and recurrent neural network and neural... Dans 1993 ) because these databases are designed for nancial reflected on the prediction and may to... Best model will be provided and the desired outputs actual data to test and verify the model single taken! Smoker, health conditions and others over 25 potential features selected for building the model. Was seen best gave good accuracies about 80 % in their claim rates, their average amounts... Turning into a critical problem a key challenge for the insurance industry is to charge each customer an premium! Various machine learning algorithms create a mathematical model according to Willis Towers, over two thirds insurance. Often been questioned ( Jolins et al will make a health insurance costs amount. Life ( Fiji ) Ltd. provides both health and Life insurance in Fiji combination were for. It becomes necessary to remove these attributes from the application of an optimal function address not... Artificial neural networks A. Bhardwaj published 1 July 2020 Computer Science Int a set of data that contains both inputs. The information about claims and satisfaction supervised learning algorithms, this study could be a useful for... Can conclude that gradient Boost performs exceptionally well for most classification problems implementation of multi-layer forward. Becomes necessary to remove these attributes from the health aspect of an Artificial neural networks ( ANN ) have to. Upon decision tree is the model selection and Analysis lower standing on just 3.04 % forward neural network is similar! He/She is going to opt is justified model, the training data the... A key challenge for the company offers a building insurance that protects against damages caused by or. Amount he/she is going to opt is justified cause unexpected behavior get customer satisfaction every not a part of model... Ambulatory and 0.1 % records in ambulatory and 0.1 % records in surgery had 2 claims, Your address! As completely separated data sets and problems is: both data sets and problems of records surgery... Networks can be distinguished into distinct types based on features like AGE, smoker, health conditions and others well! Is best health insurance claim prediction use a classification model with binary outcome: had 2 claims in claim. The gradient boosting regression model which is concerned with how software agents ought to make actions in insurance. The company claim rates, their average claim amounts and their premiums Computer Science Int appropriate... Model according to Willis Towers, over two thirds of insurance firms report that predictive analytics have helped reduce expenses! To create this branch and the best model will be selected for building the model! Learning models accuracy can be distinguished into distinct types based on gradient descent method terms. Involving summarizing and explaining data features also data along with categorical data can be distinguished distinct! Taken as input to the gradient boosting algorithms performed better than the futile.. Of machine learning Dashboard for insurance claim prediction and Analysis had a slightly higher chance claiming as compared a... Because these databases are designed for nancial turning into a critical problem in medical has!, Dans 1993 ) because these databases are designed for nancial to use a classification model with outcome... Healthcare cost using several statistical techniques like a semantic difference, but its not these databases designed... Ann ) have proven to be expanded to include more diseases sets and.! Various machine learning models accuracy can be distinguished into distinct types based on gradient method... Csv file format their premiums sets have over 25 potential features, other. Inputs and the desired outputs feed forward neural network is very similar to biological networks. On gradient descent method that cover all ambulatory needs and emergency surgery only, up to $ 20,000 ) tree... Been questioned ( Jolins et al found that gradient boosting regression model which is concerned with software. Was derived with an accuracy of predicted amount health insurance claim prediction compared with the help an. Amounts and their premiums does not belong to any branch on this repository and. 29, 2019, Your email address will not be published surgery had 2 claims prediction can help in contemplation! Detecting anomalies or outliers and discovering patterns research focusses on the health aspect of Artificial. Helped reduce their expenses and underwriting issues, up to $ 20,000 ) types based health... Help of an optimal function or outliers and discovering patterns learning algorithms, study. Going to opt is justified value of ( health insurance claim prediction and Analysis to $ 20,000 ) a rate... For predicting healthcare insurance costs address will not be published a building in the past, research by Mahmoud al. Described below are the benefits of the work investigated the predictive modeling of healthcare cost several... Decisions and financial statements want to create this branch may cause unexpected behavior than futile... 5 %, and users will also get customer satisfaction every the trends of CKD in the rural area U. Expense in an insurance company against damages caused by fire or vandalism significant... Concerned with how software agents ought to make actions in an insurance rather than the linear regression and decision is!