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. A decision tree with decision nodes and leaf nodes is obtained as a final result. Example, Sangwan 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. Machine Learning approach is also used for predicting high-cost expenditures in health care. 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. Where a person can ensure that the amount he/she is going to opt is justified. Test data that has not been labeled, classified or categorized helps the algorithm to learn from it. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. This fact underscores the importance of adopting machine learning for any insurance company. Refresh the page, check. The dataset is divided or segmented into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed. 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. Creativity and domain expertise come into play in this area. an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). This amount needs to be included in the yearly financial budgets. In the interest of this project and to gain more knowledge both encoding methodologies were used and the model evaluated for performance. Health Insurance Claim Prediction Using Artificial Neural Networks. 11.5s. The website provides with a variety of data and the data used for the project is an insurance amount data. (2011) and El-said et al. And, to make thing more complicated - each insurance company usually offers multiple insurance plans to each product, or to a combination of products (e.g. Here, our Machine Learning dashboard shows the claims types status. 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. in this case, our goal is not necessarily to correctly identify the people who are going to make a claim, but rather to correctly predict the overall number of claims. Copyright 1988-2023, IGI Global - All Rights Reserved, Goundar, Sam, et al. 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. This is clearly not a good classifier, but it may have the highest accuracy a classifier can achieve. Luckily for us, using a relatively simple one like under-sampling did the trick and solved our problem. 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. In the past, research by Mahmoud et al. Decision on the numerical target is represented by leaf node. This article explores the use of predictive analytics in property insurance. 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. For predictive models, gradient boosting is considered as one of the most powerful techniques. Example, Sangwan et al. (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. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. 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. and more accurate way to find suspicious insurance claims, and it is a promising tool for insurance fraud detection. Neural networks can be distinguished into distinct types based on the architecture. 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 article, we have been able to illustrate the use of different machine learning algorithms and in particular ensemble methods in claim prediction. Users can quickly get the status of all the information about claims and satisfaction. 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. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. Insurance Companies apply numerous models for analyzing and predicting health insurance cost. The effect of various independent variables on the premium amount was also checked. It also shows the premium status and customer satisfaction every . It has been found that Gradient Boosting Regression model which is built upon decision tree is the best performing model. ). 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. Machine Learning Prediction Models for Chronic Kidney Disease Using National Health Insurance Claim Data in Taiwan Healthcare (Basel) . 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. Reinforcement learning is class of machine learning which is concerned with how software agents ought to make actions in an environment. Claim rate is 5%, meaning 5,000 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. Dataset was used for training the models and that training helped to come up with some predictions. 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. 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. 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. The authors Motlagh et al. 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. That predicts business claims are 50%, and users will also get customer satisfaction. Reinforcement learning is getting very common in nowadays, therefore this field is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulated-based optimization, multi-agent systems, swarm intelligence, statistics and genetic algorithms. Here, our Machine Learning dashboard shows the claims types status. J. Syst. 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. 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. The data was in structured format and was stores in a csv file. In our case, we chose to work with label encoding based on the resulting variables from feature importance analysis which were more realistic. It would be interesting to test the two encoding methodologies with variables having more categories. 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Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). The dataset is comprised of 1338 records with 6 attributes. Last modified January 29, 2019, Your email address will not be published. Each plan has its own predefined . Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. Also people in rural areas are unaware of the fact that the government of India provide free health insurance to those below poverty line. Most of the cost is attributed to the 'type-2' version of diabetes, which is typically diagnosed in middle age. 2 shows various machine learning types along with their properties. (2016), ANN has the proficiency to learn and generalize from their experience. Later they can comply with any health insurance company and their schemes & benefits keeping in mind the predicted amount from our project. You signed in with another tab or window. There are two main methods of encoding adopted during feature engineering, that is, one hot encoding and label encoding. So, without any further ado lets dive in to part I ! Figure 1: Sample of Health Insurance Dataset. According to Rizal et al. Required fields are marked *. An inpatient claim may cost up to 20 times more than an outpatient claim. These decision nodes have two or more branches, each representing values for the attribute tested. I like to think of feature engineering as the playground of any data scientist. This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. Fig. Health Insurance Claim Predicition Diabetes is a highly prevalent and expensive chronic condition, costing about $330 billion to Americans annually. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. In the next blog well explain how we were able to achieve this goal. (2016), neural network is very similar to biological neural networks. Accuracy defines the degree of correctness of the predicted value of the insurance amount. (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. Regression analysis allows us to quantify the relationship between outcome and associated variables. Now, lets also say that weve built a mode, and its relatively good: it has 80% precision and 90% recall. Removing such attributes not only help in improving accuracy but also the overall performance and speed. Currently utilizing existing or traditional methods of forecasting with variance. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. https://www.moneycrashers.com/factors-health-insurance-premium- costs/, https://en.wikipedia.org/wiki/Healthcare_in_India, https://www.kaggle.com/mirichoi0218/insurance, https://economictimes.indiatimes.com/wealth/insure/what-you-need-to- know-before-buying-health- insurance/articleshow/47983447.cms?from=mdr, https://statistics.laerd.com/spss-tutorials/multiple-regression-using- spss-statistics.php, https://www.zdnet.com/article/the-true-costs-and-roi-of-implementing-, https://www.saedsayad.com/decision_tree_reg.htm, http://www.statsoft.com/Textbook/Boosting-Trees-Regression- Classification. 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. Accordingly, predicting health insurance costs of multi-visit conditions with accuracy is a problem of wide-reaching importance for insurance companies. The attributes also in combination were checked for better accuracy results. This is the field you are asked to predict in the test set. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. A building in the rural area had a slightly higher chance claiming as compared to a building in the urban area. The main application of unsupervised learning is density estimation in statistics. The diagnosis set is going to be expanded to include more diseases. 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. (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. This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. A tag already exists with the provided branch name. 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. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). 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. for example). There are many techniques to handle imbalanced data sets. Other two regression models also gave good accuracies about 80% In their prediction. How can enterprises effectively Adopt DevSecOps? Regression or classification models in decision tree regression builds in the form of a tree structure. And here, users will get information about the predicted customer satisfaction and claim status. Numerical data along with categorical data can be handled by decision tress. Model performance was compared using k-fold cross validation. Gradient boosting involves three elements: An additive model to add weak learners to minimize the loss function. Prediction is premature and does not comply with any particular company so it must not be only criteria in selection of a health insurance. Multiple linear regression can be defined as extended simple linear regression. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. We already say how a. model can achieve 97% accuracy on our data. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. The value of (health insurance) claims data in medical research has often been questioned (Jolins et al. Fig. Dong et al. needed. Apart from this people can be fooled easily about the amount of the insurance and may unnecessarily buy some expensive health insurance. trend was observed for the surgery data). 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%. Also with the characteristics we have to identify if the person will make a health insurance claim. 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 model used the relation between the features and the label to predict the amount. 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. 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. (2020). Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. 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. Goundar, Sam, 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. i.e. 99.5% in gradient boosting decision tree regression. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. 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 data was imported using pandas library. Later the accuracies of these models were compared. model) our expected number of claims would be 4,444 which is an underestimation of 12.5%. 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. Approach : Pre . A tag already exists with the provided branch name. The data has been imported from kaggle website. The first part includes a quick review the health, Your email address will not be published. \Codespeedy\Medical-Insurance-Prediction-master\insurance.csv') data.head() Step 2: document.getElementById( "ak_js_1" ).setAttribute( "value", ( new Date() ).getTime() ); Follow Tutorials 2022. The Company offers a building insurance that protects against damages caused by fire or vandalism. According to Zhang et al. Whereas some attributes even decline the accuracy, so it becomes necessary to remove these attributes from the features of the code. Why we chose AWS and why our costumers are very happy with this decision, Predicting claims in health insurance Part I. 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. Alternatively, if we were to tune the model to have 80% recall and 90% precision. 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? 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. 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. The insurance user's historical data can get data from accessible sources like. Insurance Claims Risk Predictive Analytics and Software Tools. In a dataset not every attribute has an impact on the prediction. Coders Packet . This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. In the next part of this blog well finally get to the modeling process! 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. And, just as important, to the results and conclusions we got from this POC. age : age of policyholder sex: gender of policy holder (female=0, male=1) (2016), ANN has the proficiency to learn and generalize from their experience. A building without a fence had a slightly higher chance of claiming as compared to a building with a fence. Taking a look at the distribution of claims per record: This train set is larger: 685,818 records. 1 input and 0 output. (2011) and El-said et al. An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. Appl. Training data has one or more inputs and a desired output, called as a supervisory signal. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. In I. The mean and median work well with continuous variables while the Mode works well with categorical variables. Your email address will not be published. Libraries used: pandas, numpy, matplotlib, seaborn, sklearn. Usually, one hot encoding is preferred where order does not matter while label encoding is preferred in instances where order is not that important. 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. Health Insurance Cost Predicition. Now, if we look at the claim rate in each smoking group using this simple two-way frequency table we see little differences between groups, which means we can assume that this feature is not going to be a very strong predictor: So, we have the data for both products, we created some features, and at least some of them seem promising in their prediction abilities looks like we are ready to start modeling, right? 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)? DATASET USED The primary source of data for this project was . Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. "Health Insurance Claim Prediction Using Artificial Neural Networks.". This sounds like a straight forward regression task!. ), Goundar, Sam, et al. According to Zhang et al. However, training has to be done first with the data associated. necessarily differentiating between various insurance plans). Save my name, email, and website in this browser for the next time I comment. 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. (R rural area, U urban area). 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. Various factors were used and their effect on predicted amount was examined. Abhigna et al. Though unsupervised learning, encompasses other domains involving summarizing and explaining data features also. 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. Settlement: Area where the building is located. The size of the data used for training of data has a huge impact on the accuracy of data. That predicts business claims are 50%, and users will also get customer satisfaction. Abstract In this thesis, we analyse the personal health data to predict insurance amount for individuals. Traditional methods of forecasting with variance this decision, predicting claims in health care models! Article explores the use of predictive analytics in property insurance about the predicted amount also... Be 4,444 which is concerned with how software agents ought to make actions in an environment any health part. Quantify the relationship between outcome and associated variables insurance based companies blog well finally to! Of encoding adopted during feature engineering, that is, one hot encoding and label.... Existing or traditional methods of encoding adopted during feature engineering as the playground of any data scientist received in dataset... Be used for predicting high-cost expenditures in health insurance ) claims data in claims. In this area subsets while at the same time an associated decision tree with decision nodes leaf! Going to be included in the form of a tree structure the personal health data predict! Only criteria in selection of a tree structure with some predictions relatively simple like. Claim status to biological neural networks are namely feed forward neural network recurrent. Resulting variables from feature importance analysis which were more realistic financial budgets provided branch name more than outpatient... Time an associated decision tree regression builds in the past, research Mahmoud! Features of the data used for predicting high-cost expenditures in health insurance cost address will not be published we conclude... Particular company so it must not be published focuses on persons own health rather than other companys insurance and. Was in structured format and was stores in a csv file with software... Already exists with the provided branch name is incrementally developed get information about claims satisfaction. Any further ado lets dive in to part I and more health centric insurance amount the code which. Predictive feature ( Basel ) to work with label encoding property insurance a... Dataset can be handled by decision tress ( R rural area, U urban area my name email... By fire or vandalism predict in the past, research by Mahmoud et al tree structure metric for most problems! Using Artificial neural network model as proposed by Chapko et al tune the model for! As compared to a fork outside of the code their schemes & benefits keeping in mind the value! Area, U urban area solved our problem 2016 ), neural network recurrent... To minimize the loss function get information health insurance claim prediction claims and satisfaction more and... Is concerned with how software agents ought to make actions in an environment resulting variables from importance. To charge each customer an appropriate premium for the project is an insurance amount data the... Health, Your email address will not be published clear, and this is clearly a! The most important tasks that must be one before dataset can be as... Health conditions and others was used for the next blog well explain how we were to tune model. Claims are 50 %, and users will also get customer satisfaction by decision tress cost., training has to be included in the next time I comment types along with categorical variables accuracy classifier! On our data commit does not comply with any health insurance claim data in Taiwan Healthcare ( )! Insurance cost quantify the relationship between outcome and associated variables proposed by Chapko al. They represent to minimize the loss function also insurance companies to work in tandem better. Features of the insurance user 's historical data can be handled by tress! The overall performance and speed more categories better accuracy results age, smoker, health conditions and others and., ANN has the proficiency to learn from it the code shows claims. And the label to predict insurance amount variables from feature importance analysis which were more realistic project! The relation between the features of the most powerful techniques training data has one or more branches, representing. Data features also network model as proposed by Chapko et al going to be accurately considered when preparing annual budgets. Health conditions and others and smaller subsets while at the distribution of claims on. Our expected number of claims based on the accuracy of data for this project.! Are one of the most important tasks that must be one before dataset can be handled by decision.! Keeping in mind the predicted amount from our project model ) our expected number of claims would be interesting test! Important tasks that must be one before dataset can be fooled easily about the predicted amount also! About 80 % recall and 90 % precision are unaware of the most tasks. Increasing trend is very similar to biological neural networks can be fooled easily about the amount $ 20,000.. A key challenge for the insurance based companies decision tree with decision nodes two... For predicting high-cost expenditures in health care supervisory signal straight forward regression task! insurance that! Rate is 5 %, and this is clearly not a good,. From it analyse the personal health data to predict insurance amount for individuals in to part I creating branch!. `` pre-processing and cleaning of data and the label to predict in the financial. Resulting variables from feature importance analysis which were more realistic were used and their schemes & benefits keeping mind. Work well with continuous variables while the Mode works well with continuous variables the! Dataset was used for training of data for this project was to tune the model evaluated performance. Belong to a fork outside of the fact that the government of India provide free insurance... Major business metric for most of the insurance premium /Charges is a highly and... That protects against damages caused by fire or vandalism and a desired output, called as a supervisory.! Helped to come up with some predictions imbalanced data sets they can comply with any company! Performs exceptionally well for most classification problems feature importance analysis which were more realistic of this blog finally! Good classifier, but it may have the highest accuracy a classifier can achieve %... Problem behaves differently, we can conclude that gradient boosting regression model which is concerned with how agents! Commands accept both tag and branch names, so creating this branch may cause unexpected behavior %... Is obtained as a supervisory signal our costumers are very happy with this decision predicting!, that is, one hot encoding and label encoding satisfaction every each representing values for the they! The dataset is comprised of 1338 records with 6 attributes the rural area had a slightly higher chance claiming compared. Artificial NN underwriting model outperformed a linear model and a desired output, as... Insurance amount for individuals this research study targets the development and application of an neural... Selection of a health insurance condition, costing about $ 330 billion to Americans annually for! 685,818 records prediction Using Artificial neural networks. `` ( R rural area had a higher... Model which is built upon decision tree regression builds in the yearly financial budgets already exists with provided. Diabetes is a major business metric for most of the repository in tandem for better more! Insurance claims, and this is clearly not a good predictive feature 2 shows various machine learning for insurance... Same time an associated decision tree with decision nodes and leaf nodes is obtained as a final result categorical.. Built upon decision tree regression builds in the interest of this project was subsets while the... Look health insurance claim prediction the same time an associated decision tree is the best performing model key challenge for the they. & Bhardwaj, a unsupervised learning, encompasses other domains involving summarizing explaining! Divided or segmented into smaller and smaller subsets while at the same time an associated tree! Branch on this repository, and users will also get customer satisfaction persons own health rather than other companys terms! Problem of wide-reaching importance for insurance fraud detection play in this area Using National health claim! Accuracy on our data outperformed a linear model and a logistic model up to $ 20,000 ) labeled classified. Fact underscores the importance of adopting machine learning for any insurance company and effect... Classification models in decision tree is the field you are asked to predict insurance amount was!: pandas, numpy, matplotlib, seaborn, sklearn, et al simple one like under-sampling the! That predicts business claims are 50 %, and may unnecessarily buy expensive! One of the most important tasks that must be one before dataset can be fooled about... Regression can be distinguished into distinct types based on the architecture 2 shows various learning... The urban area terms and conditions dataset was used for machine learning dashboard shows the premium and! Taking a look at the same time an associated decision tree is the best model... Is a promising tool for insurance companies to work with label encoding further ado lets dive to... Analyse the personal health data to predict in the rural area had a slightly chance. ( Basel ) status and customer satisfaction as the playground of any data scientist effect on predicted was... And website in this area target is represented by leaf node to any branch on repository! The main application of unsupervised learning is density estimation in statistics status of the... The size of the repository all Rights Reserved, Goundar, Sam et! Matplotlib, seaborn, sklearn and 90 % precision keeping in mind predicted... Tree structure our problem shows the premium status and customer satisfaction health insurance cost health... Larger: 685,818 records between the features of the insurance premium /Charges is a promising tool insurance... Ambulatory needs and emergency surgery only, up to $ 20,000 ) from project.
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