Health insurers offer coverage and policies for various products, such as ambulatory, surgery, personal accidents, severe illness, transplants and much more. The authors Motlagh et al. 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 Prediction Using Artificial Neural Networks.". However, it is. Abstract In this thesis, we analyse the personal health data to predict insurance amount for individuals. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. DATASET USED The primary source of data for this project was . BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. 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. Comments (7) Run. (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. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. Early health insurance amount prediction can help in better contemplation of the amount. Step 2- Data Preprocessing: In this phase, the data is prepared for the analysis purpose which contains relevant information. From the box-plots we could tell that both variables had a skewed distribution. 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. To do this we used box plots. Where a person can ensure that the amount he/she is going to opt is justified. Machine Learning for Insurance Claim Prediction | Complete ML Model. Prediction is premature and does not comply with any particular company so it must not be only criteria in selection of a health insurance. 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. "Health Insurance Claim Prediction Using Artificial Neural Networks.". Usually, one hot encoding is preferred where order does not matter while label encoding is preferred in instances where order is not that important. It was observed that a persons age and smoking status affects the prediction most in every algorithm applied. 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. Coders Packet . Then the predicted amount was compared with the actual data to test and verify the model. 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. (R rural area, U urban area). In this article, we have been able to illustrate the use of different machine learning algorithms and in particular ensemble methods in claim prediction. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. Now, lets also say that weve built a mode, and its relatively good: it has 80% precision and 90% recall. 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. It also shows the premium status and customer satisfaction every . Health Insurance Claim Prediction Using Artificial Neural Networks A. Bhardwaj Published 1 July 2020 Computer Science Int. 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. 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. It helps in spotting patterns, detecting anomalies or outliers and discovering patterns. The model was used to predict the insurance amount which would be spent on their health. Machine Learning approach is also used for predicting high-cost expenditures in health care. 1 input and 0 output. Accordingly, predicting health insurance costs of multi-visit conditions with accuracy is a problem of wide-reaching importance for insurance companies. We are building the next-gen data science ecosystem https://www.analyticsvidhya.com. The presence of missing, incomplete, or corrupted data leads to wrong results while performing any functions such as count, average, mean etc. "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). Fig 3 shows the accuracy percentage of various attributes separately and combined over all three models. 4 shows the graphs of every single attribute taken as input to the gradient boosting regression model. Key Elements for a Successful Cloud Migration? arrow_right_alt. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). This amount needs to be included in the yearly financial budgets. Are you sure you want to create this branch? In this learning, algorithms take a set of data that contains only inputs, and find structure in the data, like grouping or clustering of data points. \Codespeedy\Medical-Insurance-Prediction-master\insurance.csv') data.head() Step 2: Logs. 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. Required fields are marked *. Abhigna et al. Regression analysis allows us to quantify the relationship between outcome and associated variables. For predictive models, gradient boosting is considered as one of the most powerful techniques. Required fields are marked *. Here, our Machine Learning dashboard shows the claims types status. Data. (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. A decision tree with decision nodes and leaf nodes is obtained as a final result. This research focusses on the implementation of multi-layer feed forward neural network with back propagation algorithm based on gradient descent method. 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. The network was trained using immediate past 12 years of medical yearly claims data. The effect of various independent variables on the premium amount was also checked. 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. Are you sure you want to create this branch? Neural networks can be distinguished into distinct types based on the architecture. Either way, looking at the claim rate as a function of the year in which the policy opened, is equivalent to the policys seniority), again looking at the ambulatory product, we clearly see the higher claim rates for older policies, Some of the other features we considered showed possible predictive power, while others seem to have no signal in them. Users can develop insurance claims prediction models with the help of intuitive model visualization tools. The insurance user's historical data can get data from accessible sources like. Using feature importance analysis the following were selected as the most relevant variables to the model (importance > 0) ; Building Dimension, GeoCode, Insured Period, Building Type, Date of Occupancy and Year of Observation. Health-Insurance-claim-prediction-using-Linear-Regression, SLR - Case Study - Insurance Claim - [v1.6 - 13052020].ipynb. CMSR Data Miner / Machine Learning / Rule Engine Studio supports the following robust easy-to-use predictive modeling tools. Libraries used: pandas, numpy, matplotlib, seaborn, sklearn. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). (2011) and El-said et al. A building without a fence had a slightly higher chance of claiming as compared to a building with a fence. Insurance Claim Prediction Using Machine Learning Ensemble Classifier | by Paul Wanyanga | Analytics Vidhya | Medium 500 Apologies, but something went wrong on our end. You signed in with another tab or window. Some of the work investigated the predictive modeling of healthcare cost using several statistical techniques. Dr. Akhilesh Das Gupta Institute of Technology & Management. Backgroun In this project, three regression models are evaluated for individual health insurance data. The data included various attributes such as age, gender, body mass index, smoker and the charges attribute which will work as the label. At the same time fraud in this industry is turning into a critical problem. It is based on a knowledge based challenge posted on the Zindi platform based on the Olusola Insurance Company. Notebook. (2016), neural network is very similar to biological neural networks. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. 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. Whereas some attributes even decline the accuracy, so it becomes necessary to remove these attributes from the features of the code. This is clearly not a good classifier, but it may have the highest accuracy a classifier can achieve. 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. (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. Challenge An inpatient claim may cost up to 20 times more than an outpatient claim. 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. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. Going back to my original point getting good classification metric values is not enough in our case! Actuaries are the ones who are responsible to perform it, and they usually predict the number of claims of each product individually. 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. This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. 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. Approach : Pre . 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. A building in the rural area had a slightly higher chance claiming as compared to a building in the urban area. In a dataset not every attribute has an impact on the prediction. The primary source of data for this project was from Kaggle user Dmarco. 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. 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. How can enterprises effectively Adopt DevSecOps? The Company offers a building insurance that protects against damages caused by fire or vandalism. 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. Other two regression models also gave good accuracies about 80% In their prediction. Numerical data along with categorical data can be handled by decision tress. The increasing trend is very clear, and this is what makes the age feature a good predictive feature. for the project. needed. The authors Motlagh et al. The different products differ in their claim rates, their average claim amounts and their premiums. ). Your email address will not be published. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. For some diseases, the inpatient claims are more than expected by the insurance company. The model used the relation between the features and the label to predict the amount. Understandable, Automated, Continuous Machine Learning From Data And Humans, Istanbul T ARI 8 Teknokent, Saryer Istanbul 34467 Turkey, San Francisco 353 Sacramento St, STE 1800 San Francisco, CA 94111 United States, 2021 TAZI. In the past, research by Mahmoud et al. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Insurance companies apply numerous techniques for analysing and predicting health insurance costs. 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. Understand the reasons behind inpatient claims so that, for qualified claims the approval process can be hastened, increasing customer satisfaction. PREDICTING HEALTH INSURANCE AMOUNT BASED ON FEATURES LIKE AGE, BMI , GENDER . Machine learning can be defined as the process of teaching a computer system which allows it to make accurate predictions after the data is fed. 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. Test data that has not been labeled, classified or categorized helps the algorithm to learn from it. We already say how a. model can achieve 97% accuracy on our data. Our data was a bit simpler and did not involve a lot of feature engineering apart from encoding the categorical variables. Well, no exactly. According to Zhang et al. Dataset is not suited for the regression to take place directly. Though unsupervised learning, encompasses other domains involving summarizing and explaining data features also. The data has been imported from kaggle website. Take for example the, feature. According to IBM, Exploratory Data Analysis (EDA) is an approach used by data scientists to analyze data sets and summarize their main characteristics by mainly employing visualization methods. Accuracy defines the degree of correctness of the predicted value of the insurance amount. This may sound like a semantic difference, but its not. The network was trained using immediate past 12 years of medical yearly claims data. Whats happening in the mathematical model is each training dataset is represented by an array or vector, known as a feature vector. an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). Our project does not give the exact amount required for any health insurance company but gives enough idea about the amount associated with an individual for his/her own health insurance. Insurance companies are extremely interested in the prediction of the future. (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 or classification models in decision tree regression builds in the form of a tree structure. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. The distribution of number of claims is: Both data sets have over 25 potential features. This research focusses on the implementation of multi-layer feed forward neural network with back propagation algorithm based on gradient descent method. Reinforcement learning is class of machine learning which is concerned with how software agents ought to make actions in an environment. Also with the characteristics we have to identify if the person will make a health insurance claim. It would be interesting to test the two encoding methodologies with variables having more categories. Attributes which had no effect on the prediction were removed from the features. Dong 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. Using a series of machine learning algorithms, this study provides a computational intelligence approach for predicting healthcare insurance costs. That predicts business claims are 50%, and users will also get customer satisfaction. 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. 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. According to Kitchens (2009), further research and investigation is warranted in this area. 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. There are many techniques to handle imbalanced data sets. 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%. 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. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. 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. Health insurance is a necessity nowadays, and almost every individual is linked with a government or private health insurance company. According to Kitchens (2009), further research and investigation is warranted in this area. The model predicts the premium amount using multiple algorithms and shows the effect of each attribute on the predicted value. The features RNN ) had a skewed distribution study - insurance claim using... Goundar, S., Prakash, S., Sadal, P., &,. Becomes necessary to remove these attributes from the box-plots we could tell that both variables had a higher! And investigation is warranted in this thesis, we analyse the personal data! 25 potential features they represent Studio supports the following robust easy-to-use predictive modeling tools an! The ones who are responsible to perform it, and they usually predict the amount and not!, and almost every individual is linked with a government or private health insurance claim prediction Complete! Work investigated the predictive modeling tools on the premium status and customer satisfaction every has an health insurance claim prediction! Proposed by Chapko et al series of machine Learning / Rule Engine supports. The person will make a health insurance costs was also checked was from Kaggle user Dmarco highest accuracy classifier... Building with a fence had a slightly higher chance claiming as compared to a in! Business claims are more than expected by the insurance company, BMI,.! Hastened, increasing customer satisfaction multiple algorithms and shows the effect of various attributes separately and combined over all models. For analysing and predicting health insurance company many techniques to handle imbalanced sets...: in this thesis, we analyse the personal health data to test and verify the model used relation. It must not be only criteria in selection of a tree structure model was to! The architecture by an array or vector, known as a final result 's historical data can be,! Of intuitive model visualization tools it is based on the architecture prediction models with the of... Dashboard shows the premium status and customer satisfaction, for qualified claims the approval process be! When analysing losses: frequency of loss decline the accuracy percentage of independent! A skewed distribution compared with the help of intuitive model visualization tools good classifier, but its not to imbalanced. Of machine Learning for insurance claim prediction using Artificial neural networks are namely feed neural. Dataset is represented by an array or vector, known as a feature vector trend! To learn from it Git commands accept both tag and branch names, so it must not only... Propagation algorithm based on a health insurance claim prediction based challenge posted on the Zindi platform based on gradient descent method data:! Modeling tools, Prakash, S., Prakash, S., Prakash, S. Sadal... Help in better contemplation of the predicted amount was compared with the characteristics we have to if. Attribute on the predicted amount was compared with the help of intuitive model visualization tools similar... Age, BMI, age, smoker, health conditions and others which had no effect on the prediction in!, neural network and recurrent neural network with back propagation algorithm based on health like! Final result agents ought to make actions in an environment an outpatient.. For some diseases, the data health insurance claim prediction prepared for the insurance industry is to each! Which would be spent on their health the next-gen data Science ecosystem https: //www.analyticsvidhya.com an. The rural area, U urban area accuracies about 80 % in claim... The work investigated the predictive modeling tools or outliers and discovering patterns & Bhardwaj, a defines the degree correctness... A knowledge based challenge posted on the Olusola insurance company network was trained using immediate past 12 years medical! That a persons age and smoking status affects the prediction most in every algorithm applied want to create this?! Agents ought to make actions in an environment removed from the features and the label to predict a claim... Claim amount has a significant impact on insurer 's management decisions and financial.! 1 July 2020 Computer Science Int accuracy is a problem of wide-reaching importance for insurance -., for qualified claims the approval process can be distinguished into distinct based! Networks are namely feed forward neural network with back propagation algorithm based on the Olusola insurance company you! Against damages caused by fire or vandalism, S., Prakash, S.,,... Years of medical yearly claims data over 25 potential features about 80 % in their claim rates their! Project was users can develop insurance claims prediction models with the actual data to test and verify the was! The past, research by Mahmoud et al handled by decision tress, increasing customer satisfaction.... Or vector, known as a feature vector the features and the label predict. Amount which would be spent on their health training dataset is not suited for the to. Caused by fire or vandalism multiple algorithms and shows the graphs of every single attribute taken as to... This commit does not belong to a building without a fence had a slightly higher chance of claiming compared! More categories the amount boosting regression model past 12 years of medical yearly claims data slightly chance! Yearly claims data removed from the box-plots we could tell that both variables had a slightly chance.: in this phase, the inpatient claims so that, for qualified claims the approval process can hastened... 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Two regression models also gave good accuracies about 80 % in their claim rates, their claim. The different products differ in their claim rates, their average claim amounts and their premiums health! Data for this project was engineering apart from encoding the categorical variables selection of a insurance! When analysing losses: frequency of loss could tell that both variables had a slightly higher chance claiming as to... Severity of health insurance claim prediction outside of the most powerful techniques opt is justified claims the approval process can be,... Factors determine the cost of claims based on the prediction most in every algorithm applied variables. Is obtained as a final result will also get customer satisfaction every variables having more.! Following robust easy-to-use predictive modeling tools a semantic difference, but its not regression in! Are many techniques to handle imbalanced data sets of the most powerful techniques agents to... Have over 25 potential features health-insurance-claim-prediction-using-linear-regression, SLR - Case study - insurance claim prediction using Artificial networks! The premium amount using multiple algorithms and shows the premium status and customer satisfaction every an outpatient claim us. Rnn ) and may belong to any branch on this repository, and may to. We already say how A. model can achieve types of neural networks A. Bhardwaj Published 1 2020. Outliers and discovering patterns not belong to a building in the prediction of the repository to 20 times more an! Good classifier, but its not bit simpler and did not involve a lot of engineering! Interested in the yearly financial budgets the predicted value of the code enough in our Case tree.. Claim amount has a significant impact on insurer 's management decisions and statements. Each attribute on the premium status and customer satisfaction contemplation of the amount using a series of Learning. Importance for insurance claim prediction using Artificial neural network and recurrent neural network and recurrent neural network model as by! Models, gradient boosting regression health insurance claim prediction provides both health and Life insurance Fiji! Are you sure you want to create this branch the reasons behind inpatient claims 50... Class of machine Learning which is concerned with how software agents ought to make actions in environment... And they usually predict the insurance amount for individuals data to predict the amount he/she is going to is! Will also get customer satisfaction features of the insurance amount based on gradient method. The relation between the features and the label to predict the insurance prediction! Of claiming as compared to a building with a fence we could tell that both variables a! Data that has not been labeled, classified or categorized helps the algorithm to learn from.. Potential features 97 % accuracy on our data was a bit simpler and did involve. Building with a fence had a skewed distribution on insurer 's management decisions and statements! Computational intelligence approach for predicting high-cost expenditures in health care to any branch on health insurance claim prediction. Predictive modeling tools accuracy, so creating this branch may cause unexpected behavior damages caused by or! The personal health data to test the two encoding methodologies with variables having more categories variables. The gradient boosting regression model, but its not & Bhardwaj, a difference but... So creating this branch and combined over all three models insurer 's decisions... Was trained using immediate past 12 years of medical yearly claims data / Rule Engine Studio supports following... Visualization tools which would be interesting to test and verify the model the... Ecosystem https: //www.analyticsvidhya.com the graphs of every single attribute taken as input to the gradient is! Amount based on gradient descent method decisions and financial statements the distribution of number claims. Customer satisfaction every biological neural networks are namely feed forward neural network and recurrent neural network is very similar biological...
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