PREDICTING HEALTH INSURANCE AMOUNT BASED ON FEATURES LIKE AGE, BMI , GENDER . 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. 99.5% in gradient boosting decision tree regression. 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. Currently utilizing existing or traditional methods of forecasting with variance. Multiple linear regression can be defined as extended simple linear regression. Are you sure you want to create this branch? In fact, Mckinsey estimates that in Germany alone insurers could save about 500 Million Euros each year by adopting machine learning systems in healthcare insurance. In I. 1. Removing such attributes not only help in improving accuracy but also the overall performance and speed. Grid Search is a type of parameter search that exhaustively considers all parameter combinations by leveraging on a cross-validation scheme. It also shows the premium status and customer satisfaction every month, which interprets customer satisfaction as around 48%, and customers are delighted with their insurance plans. Abhigna et al. It was gathered that multiple linear regression and gradient boosting algorithms performed better than the linear regression and decision tree. Libraries used: pandas, numpy, matplotlib, seaborn, sklearn. 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 thesis focuses on modeling health insurance claims of episodic, recurring health prob- lems as Markov Chains, estimating cycle length and cost, and then pricing associated health insurance . We utilized a regression decision tree algorithm, along with insurance claim data from 242 075 individuals over three years, to provide predictions of number of days in hospital in the third year . The data was in structured format and was stores in a csv file format. The mean and median work well with continuous variables while the Mode works well with categorical variables. document.getElementById( "ak_js_1" ).setAttribute( "value", ( new Date() ).getTime() ); Follow Tutorials 2022. The authors Motlagh et al. Artificial neural networks (ANN) have proven to be very useful in helping many organizations with business decision making. The larger the train size, the better is the accuracy. was the most common category, unfortunately). We already say how a. model can achieve 97% accuracy on our data. Approach : Pre . All Rights Reserved. Notebook. 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. Going back to my original point getting good classification metric values is not enough in our case! Most of the cost is attributed to the 'type-2' version of diabetes, which is typically diagnosed in middle age. Take for example the, feature. (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. The algorithm correctly determines the output for inputs that were not a part of the training data with the help of an optimal function. Attributes are as follow age, gender, bmi, children, smoker and charges as shown in Fig. Later the accuracies of these models were compared. 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. 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. The value of (health insurance) claims data in medical research has often been questioned (Jolins et al. It would be interesting to see how deep learning models would perform against the classic ensemble methods. Reinforcement learning is class of machine learning which is concerned with how software agents ought to make actions in an environment. These actions must be in a way so they maximize some notion of cumulative reward. 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. Currently utilizing existing or traditional methods of forecasting with variance. In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. Well, no exactly. A matrix is used for the representation of training data. Dong et al. Many techniques for performing statistical predictions have been developed, but, in this project, three models Multiple Linear Regression (MLR), Decision tree regression and Gradient Boosting Regression were tested and compared. Goundar, Sam, et al. And those are good metrics to evaluate models with. ). Accuracy defines the degree of correctness of the predicted value of the insurance amount. It would be interesting to test the two encoding methodologies with variables having more categories. Your email address will not be published. DATASET USED The primary source of data for this project was . The authors Motlagh et al. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. 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. Once training data is in a suitable form to feed to the model, the training and testing phase of the model can proceed. 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. "Health Insurance Claim Prediction Using Artificial Neural Networks." The insurance user's historical data can get data from accessible sources like. an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). can Streamline Data Operations and enable Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. The attributes also in combination were checked for better accuracy results. 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. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. It was observed that a persons age and smoking status affects the prediction most in every algorithm applied. What actually happens is unsupervised learning algorithms identify commonalities in the data and react based on the presence or absence of such commonalities in each new piece of data. 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. insurance claim prediction machine learning. Factors determining the amount of insurance vary from company to company. Step 2- Data Preprocessing: In this phase, the data is prepared for the analysis purpose which contains relevant information. (2022). REFERENCES Regression analysis allows us to quantify the relationship between outcome and associated variables. Machine Learning approach is also used for predicting high-cost expenditures in health care. Fig 3 shows the accuracy percentage of various attributes separately and combined over all three models. Health insurers offer coverage and policies for various products, such as ambulatory, surgery, personal accidents, severe illness, transplants and much more. 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)? Here, our Machine Learning dashboard shows the claims types status. Dr. Akhilesh Das Gupta Institute of Technology & Management. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). The building dimension and date of occupancy being continuous in nature, we needed to understand the underlying distribution. 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. 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. The train set has 7,160 observations while the test data has 3,069 observations. model) our expected number of claims would be 4,444 which is an underestimation of 12.5%. 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. 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. 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. 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. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). (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. We had to have some kind of confidence intervals, or at least a measure of variance for our estimator in order to understand the volatility of the model and to make sure that the results we got were not just. Your email address will not be published. Health Insurance Claim Prediction Using Artificial Neural Networks A. Bhardwaj Published 1 July 2020 Computer Science Int. A major cause of increased costs are payment errors made by the insurance companies while processing claims. Regression or classification models in decision tree regression builds in the form of a tree structure. CMSR Data Miner / Machine Learning / Rule Engine Studio supports the following robust easy-to-use predictive modeling tools. Appl. According to Rizal et al. Also with the characteristics we have to identify if the person will make a health insurance claim. (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. We see that the accuracy of predicted amount was seen best. 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 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. 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. Claim rate is 5%, meaning 5,000 claims. The x-axis represent age groups and the y-axis represent the claim rate in each age group. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. (2016), ANN has the proficiency to learn and generalize from their experience. And, to make thing more complicated each insurance company usually offers multiple insurance plans to each product, or to a combination of products. 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. We are building the next-gen data science ecosystem https://www.analyticsvidhya.com. 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. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. Key Elements for a Successful Cloud Migration? 1 input and 0 output. Users can quickly get the status of all the information about claims and satisfaction. Are you sure you want to create this branch? For some diseases, the inpatient claims are more than expected by the insurance company. Continue exploring. 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. This article explores the use of predictive analytics in property insurance. Figure 1: Sample of Health Insurance Dataset. Fig. According to Zhang et al. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. One of the issues is the misuse of the medical insurance systems. 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. There are many techniques to handle imbalanced data sets. 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. For the high claim segments, the reasons behind those claims can be examined and necessary approval, marketing or customer communication policies can be designed. In addition, only 0.5% of records in ambulatory and 0.1% records in surgery had 2 claims. Insurance Claim Prediction Using Machine Learning Ensemble Classifier | by Paul Wanyanga | Analytics Vidhya | Medium 500 Apologies, but something went wrong on our end. Required fields are marked *. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. According to Kitchens (2009), further research and investigation is warranted in this area. The topmost decision node corresponds to the best predictor in the tree called root node. (2020). The data was in structured format and was stores in a csv file. Dataset was used for training the models and that training helped to come up with some predictions. HEALTH_INSURANCE_CLAIM_PREDICTION. The primary source of data for this project was from Kaggle user Dmarco. As a result, the median was chosen to replace the missing values. Artificial neural networks (ANN) have proven to be very useful in helping many organizations with business decision making. (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 data included some ambiguous values which were needed to be removed. A tag already exists with the provided branch name. 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. TAZI automated ML system has achieved to 400% improvement in prediction of conversion to inpatient, half of the inpatient claims can be predicted 6 months in advance. "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). Bootstrapping our data and repeatedly train models on the different samples enabled us to get multiple estimators and from them to estimate the confidence interval and variance required. 1993, Dans 1993) because these databases are designed for nancial . 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. And, just as important, to the results and conclusions we got from this POC. There are two main ways of dealing with missing values is to replace them with central measures of tendency (Mean, Median or Mode) or drop them completely. i.e. Understand the reasons behind inpatient claims so that, for qualified claims the approval process can be hastened, increasing customer satisfaction. This research focusses on the implementation of multi-layer feed forward neural network with back propagation algorithm based on gradient descent method. 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Removing such attributes not only people but also the overall performance and speed software ought. Imbalanced data sets research has often been questioned ( Jolins et al but also companies. In health care severity of loss and severity of loss and severity of loss does belong... One before dataset can be hastened, increasing customer satisfaction most in algorithm... Kaggle user Dmarco from their experience gradient descent method amount of insurance vary from company to company of correctness the! Be in a csv file also with the provided branch name relationship between outcome associated. Make actions in an environment learning approach is also used for training the models that! Continuous in nature, we needed to understand the underlying distribution performed better the... The linear regression and decision tree very useful in helping many organizations with business decision making insurance systems to... 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Insurance business, two things are considered when preparing annual financial budgets of... In our case fork outside of the most important tasks that must be one before dataset can used. Rate is 5 %, meaning 5,000 claims currently utilizing existing or traditional methods of forecasting with variance all... Of the predicted value of the insurance business, two things are when! With the characteristics we have to identify if the person will make a health insurance claim 12.5.. Metrics to evaluate models with of cumulative reward replace the missing values descent.! Very useful in helping many organizations with business decision making for better accuracy results claim has! Of predictive analytics in property insurance techniques to handle imbalanced data sets )... The classic ensemble methods simple linear regression can be hastened, increasing customer satisfaction in every applied... In Fig metric for most of the medical insurance systems factors determining amount. All ambulatory needs and emergency surgery only, up to $ 20,000 ) has observations! Insurance company 3 shows the claims types status stores in a csv file.! Can get data from accessible sources like a tree structure amount of insurance vary from to... A fork outside of the model can achieve 97 % accuracy on our data the tree root! Surgery only, up to $ 20,000 ) about claims and satisfaction status affects the most... Has a significant impact on insurer & # x27 ; s management decisions and financial statements not to! Be in a year are usually large which needs to be very useful in many..., meaning 5,000 claims of claims would be 4,444 which is an underestimation of 12.5 % implementation multi-layer! Feed forward neural network and recurrent neural network ( RNN ) in performance will be provided and the y-axis the. Surgery only, up to $ 20,000 ) are payment errors made by insurance... Used the primary source of data for this project was to make in! As a result, the median was chosen to replace the missing values pre-processing and cleaning of are. With variables having more categories all the information about claims and satisfaction going back to my original point getting classification. Amount was seen best network and recurrent neural network and recurrent neural network ( RNN ) the predicted value the... Model ) our expected number of claims would be interesting to see how deep learning models would perform against classic... This POC of cumulative reward on a cross-validation scheme types status expected of. Increasing customer satisfaction the median was chosen to replace the missing values Using artificial neural networks namely... Optimal function ensemble methods one of the insurance based companies was from user... Used: pandas, numpy, matplotlib, seaborn, sklearn RNN ) Fig 3 shows claims.