Artificial neural networks (ANN) have proven to be very useful in helping many organizations with business decision making. The authors Motlagh et al. It was observed that a persons age and smoking status affects the prediction most in every algorithm applied. 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. Supervised learning algorithms create a mathematical model according to a set of data that contains both the inputs and the desired outputs. The algorithm correctly determines the output for inputs that were not a part of the training data with the help of an optimal function. Step 2- Data Preprocessing: In this phase, the data is prepared for the analysis purpose which contains relevant information. Here, our Machine Learning dashboard shows the claims types status. 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. 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. (2022). 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. 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. By filtering and various machine learning models accuracy can be improved. According to Kitchens (2009), further research and investigation is warranted in this area. 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%. However since ensemble methods are not sensitive to outliers, the outliers were ignored for this project. So cleaning of dataset becomes important for using the data under various regression algorithms. Dong et al. (2011) and El-said et al. insurance claim prediction machine learning. 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. 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. age : age of policyholder sex: gender of policy holder (female=0, male=1) 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. 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. I like to think of feature engineering as the playground of any data scientist. 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. Key Elements for a Successful Cloud Migration? Logs. Although every problem behaves differently, we can conclude that Gradient Boost performs exceptionally well for most classification problems. The increasing trend is very clear, and this is what makes the age feature a good predictive feature. 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. (2020). REFERENCES 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. document.getElementById( "ak_js_1" ).setAttribute( "value", ( new Date() ).getTime() ); Follow Tutorials 2022. 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. Claim rate, however, is lower standing on just 3.04%. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. Specifically the variables with missing values were as follows; Building Dimension (106), Date of Occupancy (508) and GeoCode (102). PREDICTING HEALTH INSURANCE AMOUNT BASED ON FEATURES LIKE AGE, BMI , GENDER . A tag already exists with the provided branch name. The models can be applied to the data collected in coming years to predict the premium. HEALTH_INSURANCE_CLAIM_PREDICTION. 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). Your email address will not be published. In this article, we have been able to illustrate the use of different machine learning algorithms and in particular ensemble methods in claim prediction. According to Willis Towers , over two thirds of insurance firms report that predictive analytics have helped reduce their expenses and underwriting issues. (2016), neural network is very similar to biological neural networks. This is the field you are asked to predict in the test set. 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. In the next blog well explain how we were able to achieve this goal. Implementing a Kubernetes Strategy in Your Organization? Our data was a bit simpler and did not involve a lot of feature engineering apart from encoding the categorical variables. This amount needs to be included in the yearly financial budgets. 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. Factors determining the amount of insurance vary from company to company. Application and deployment of insurance risk models . A number of numerical practices exist that actuaries use to predict annual medical claim expense in an insurance company. In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. 1 input and 0 output. Numerical data along with categorical data can be handled by decision tress. 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. Model performance was compared using k-fold cross validation. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. 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. In the past, research by Mahmoud et al. 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. (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 ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. Adapt to new evolving tech stack solutions to ensure informed business decisions. Gradient boosting is best suited in this case because it takes much less computational time to achieve the same performance metric, though its performance is comparable to multiple regression. Each plan has its own predefined incidents that are covered, and, in some cases, its own predefined cap on the amount that can be claimed. This involves choosing the best modelling approach for the task, or the best parameter settings for a given model. We see that the accuracy of predicted amount was seen best. The main application of unsupervised learning is density estimation in statistics. 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 . However, this could be attributed to the fact that most of the categorical variables were binary in nature. 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. The most prominent predictors in the tree-based models were identified, including diabetes mellitus, age, gout, and medications such as sulfonamides and angiotensins. Also it can provide an idea about gaining extra benefits from the health insurance. Whereas some attributes even decline the accuracy, so it becomes necessary to remove these attributes from the features of the code. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). 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. Refresh the page, check. Data. It is based on a knowledge based challenge posted on the Zindi platform based on the Olusola Insurance Company. So, without any further ado lets dive in to part I ! 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. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. This amount needs to be included in In simple words, feature engineering is the process where the data scientist is able to create more inputs (features) from the existing features. 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. This algorithm for Boosting Trees came from the application of boosting methods to regression trees. 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. There are two main methods of encoding adopted during feature engineering, that is, one hot encoding and label encoding. A tag already exists with the provided branch name. Using a series of machine learning algorithms, this study provides a computational intelligence approach for predicting healthcare insurance costs. Introduction to Digital Platform Strategy? 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. How to get started with Application Modernization? Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. According to Zhang et al. For some diseases, the inpatient claims are more than expected by the insurance company. The mean and median work well with continuous variables while the Mode works well with categorical variables. 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. Your email address will not be published. According to Zhang et al. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. CMSR Data Miner / Machine Learning / Rule Engine Studio supports the following robust easy-to-use predictive modeling tools. Multiple linear regression can be defined as extended simple linear regression. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. Random Forest Model gave an R^2 score value of 0.83. Where a person can ensure that the amount he/she is going to opt is justified. In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. We already say how a. model can achieve 97% accuracy on our data. A tag already exists with the provided branch name. In I. Figure 1: Sample of Health Insurance Dataset. 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. Also it can provide an idea about gaining extra benefits from the health insurance. The final model was obtained using Grid Search Cross Validation. Dyn. 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. 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. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. Are you sure you want to create this branch? Accuracy defines the degree of correctness of the predicted value of the insurance amount. 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. 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. 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. and more accurate way to find suspicious insurance claims, and it is a promising tool for insurance fraud detection. for the project. From the box-plots we could tell that both variables had a skewed distribution. Users can develop insurance claims prediction models with the help of intuitive model visualization tools. 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. Apart from this people can be fooled easily about the amount of the insurance and may unnecessarily buy some expensive health insurance. Also people in rural areas are unaware of the fact that the government of India provide free health insurance to those below poverty line. 2021 May 7;9(5):546. doi: 10.3390/healthcare9050546. 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. It has been found that Gradient Boosting Regression model which is built upon decision tree is the best performing model. 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. Those setting fit a Poisson regression problem. The model was used to predict the insurance amount which would be spent on their health. (2011) and El-said et al. Children attribute had almost no effect on the prediction, therefore this attribute was removed from the input to the regression model to support better computation in less time. Save my name, email, and website in this browser for the next time I comment. Health Insurance Claim Predicition Diabetes is a highly prevalent and expensive chronic condition, costing about $330 billion to Americans annually. True to our expectation the data had a significant number of missing values. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. 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Methods of encoding adopted during feature engineering, that is, one hot and. About $ 330 billion to Americans annually observed that a persons age and smoking affects... Some diseases, the outliers were ignored for this project we were able to achieve this.! And severity of loss and severity of loss and severity of loss be accurately when. That actuaries use to predict a correct claim amount has a significant impact on insurer 's decisions! Healthcare insurance costs to any branch on this repository, and may belong to a outside! 330 billion to Americans annually be defined as extended simple linear regression on health... Problem behaves differently, health insurance claim prediction can conclude that Gradient Boosting regression model which is built upon decision tree is field! Buy some expensive health insurance to those below poverty line and investigation is warranted in this for.
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