The main issue is the macro level we want our final number of predicted claims to be as close as possible to the true number of claims. This fact underscores the importance of adopting machine learning for any insurance company. 4 shows the graphs of every single attribute taken as input to the gradient boosting regression model. Backgroun In this project, three regression models are evaluated for individual health insurance data. Insurance Companies apply numerous models for analyzing and predicting health insurance cost. 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. (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. How can enterprises effectively Adopt DevSecOps? Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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. REFERENCES This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 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)? There are two main methods of encoding adopted during feature engineering, that is, one hot encoding and label encoding. Reinforcement learning is class of machine learning which is concerned with how software agents ought to make actions in an environment. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. 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). 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. Continue exploring. The primary source of data for this project was from Kaggle user Dmarco. 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 addition, only 0.5% of records in ambulatory and 0.1% records in surgery had 2 claims. Logs. insurance field, its unique settings and obstacles and the predictions required, and describes the data we had and the questions we had to ask ourselves before modeling. document.getElementById( "ak_js_1" ).setAttribute( "value", ( new Date() ).getTime() ); Follow Tutorials 2022. However, training has to be done first with the data associated. 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 can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. Dr. Akhilesh Das Gupta Institute of Technology & Management. A building without a garden had a slightly higher chance of claiming as compared to a building with a garden. 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. Accuracy defines the degree of correctness of the predicted value of the insurance amount. This amount needs to be included in the yearly financial budgets. In health insurance many factors such as pre-existing body condition, family medical history, Body Mass Index (BMI), marital status, location, past insurances etc affects the amount. Whats happening in the mathematical model is each training dataset is represented by an array or vector, known as a feature vector. arrow_right_alt. Machine Learning approach is also used for predicting high-cost expenditures in health care. 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? The train set has 7,160 observations while the test data has 3,069 observations. "Health Insurance Claim Prediction Using Artificial Neural Networks." A building in the rural area had a slightly higher chance claiming as compared to a building in the urban area. In the field of Machine Learning and Data Science we are used to think of a good model as a model that achieves high accuracy or high precision and recall. Claim rate, however, is lower standing on just 3.04%. 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. Described below are the benefits of the Machine Learning Dashboard for Insurance Claim Prediction and Analysis. (2011) and El-said et al. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. The model proposed in this study could be a useful tool for policymakers in predicting the trends of CKD in the population. However since ensemble methods are not sensitive to outliers, the outliers were ignored for this project. Take for example the, feature. The model used the relation between the features and the label to predict the amount. The models can be applied to the data collected in coming years to predict the premium. Health Insurance Cost Predicition. To do this we used box plots. This is the field you are asked to predict in the test set. (2016), neural network is very similar to biological neural networks. The data was in structured format and was stores in a csv file format. Keywords Regression, Premium, Machine Learning. Goundar, Sam, et al. 99.5% in gradient boosting decision tree regression. Are you sure you want to create this branch? Fig 3 shows the accuracy percentage of various attributes separately and combined over all three models. Fig. Accurate prediction gives a chance to reduce financial loss for the company. A tag already exists with the provided branch name. Insurance Claim Prediction Using Machine Learning Ensemble Classifier | by Paul Wanyanga | Analytics Vidhya | Medium 500 Apologies, but something went wrong on our end. Last modified January 29, 2019, Your email address will not be published. Also with the characteristics we have to identify if the person will make a health insurance claim. J. Syst. 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. (2011) and El-said et al. Data. A tag already exists with the provided branch name. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. The data included some ambiguous values which were needed to be removed. ). Also it can provide an idea about gaining extra benefits from the health insurance. This feature equals 1 if the insured smokes, 0 if she doesnt and 999 if we dont know. This may sound like a semantic difference, but its not. Multiple linear regression can be defined as extended simple linear regression. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. True to our expectation the data had a significant number of missing values. It is based on a knowledge based challenge posted on the Zindi platform based on the Olusola Insurance Company. Luckily for us, using a relatively simple one like under-sampling did the trick and solved our problem. The attributes also in combination were checked for better accuracy results. arrow_right_alt. 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. Early health insurance amount prediction can help in better contemplation of the amount. Here, our Machine Learning dashboard shows the claims types status. Example, Sangwan et al. In I. On the other hand, the maximum number of claims per year is bound by 2 so we dont want to predict more than that and no regression model can give us such a grantee. Currently utilizing existing or traditional methods of forecasting with variance. Abhigna 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. 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. "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). Approach : Pre . 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. Required fields are marked *. Refresh the page, check. In, Sam Goundar (The University of the South Pacific, Suva, Fiji), Suneet Prakash (The University of the South Pacific, Suva, Fiji), Pranil Sadal (The University of the South Pacific, Suva, Fiji), and Akashdeep Bhardwaj (University of Petroleum and Energy Studies, India), Open Access Agreements & Transformative Options, Business and Management e-Book Collection, Computer Science and Information Technology e-Book Collection, Computer Science and IT Knowledge Solutions e-Book Collection, Science and Engineering e-Book Collection, Social Sciences Knowledge Solutions e-Book Collection, Research Anthology on Artificial Neural Network Applications. Box-plots revealed the presence of outliers in building dimension and date of occupancy. 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. trend was observed for the surgery data). 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 . All Rights Reserved. for example). Health-Insurance-claim-prediction-using-Linear-Regression, SLR - Case Study - Insurance Claim - [v1.6 - 13052020].ipynb. 1 input and 0 output. Notebook. You signed in with another tab or window. 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. Adapt to new evolving tech stack solutions to ensure informed business decisions. Currently utilizing existing or traditional methods of forecasting with variance. 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. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Your email address will not be published. Abhigna et al. 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. Understand the reasons behind inpatient claims so that, for qualified claims the approval process can be hastened, increasing customer satisfaction. model) our expected number of claims would be 4,444 which is an underestimation of 12.5%. 2 shows various machine learning types along with their properties. Insurance companies apply numerous techniques for analyzing and predicting health insurance costs. Where a person can ensure that the amount he/she is going to opt is justified. 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. 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. It helps in spotting patterns, detecting anomalies or outliers and discovering patterns. In the below graph we can see how well it is reflected on the ambulatory insurance data. Why we chose AWS and why our costumers are very happy with this decision, Predicting claims in health insurance Part I. 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. In the past, research by Mahmoud et al. (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. 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. We found out that while they do have many differences and should not be modeled together they also have enough similarities such that the best methodology for the Surgery analysis was also the best for the Ambulatory insurance. 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. 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: 10.4018/IJSDA.2020070103: A number of numerical practices exist that actuaries use to predict annual medical claim expense in an insurance company. A building without a fence had a slightly higher chance of claiming as compared to a building with a fence. \Codespeedy\Medical-Insurance-Prediction-master\insurance.csv') data.head() Step 2: Attributes which had no effect on the prediction were removed from the features. According to Rizal et al. A tag already exists with the provided branch name. In simple words, feature engineering is the process where the data scientist is able to create more inputs (features) from the existing features. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. effective Management. 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 can help a person in focusing more on the health aspect of an insurance rather than the futile part. Some of the work investigated the predictive modeling of healthcare cost using several statistical techniques. A major cause of increased costs are payment errors made by the insurance companies while processing claims. history Version 2 of 2. The data was imported using pandas library. In this paper, a method was developed, using large-scale health insurance claims data, to predict the number of hospitalization days in a population. 2021 May 7;9(5):546. doi: 10.3390/healthcare9050546. Appl. Decision on the numerical target is represented by leaf node. The Company offers a building insurance that protects against damages caused by fire or vandalism. Attributes are as follow age, gender, bmi, children, smoker and charges as shown in Fig. Claim rate is 5%, meaning 5,000 claims. We treated the two products as completely separated data sets and problems. 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. 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. Other two regression models also gave good accuracies about 80% In their prediction. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Random Forest Model gave an R^2 score value of 0.83. These claim amounts are usually high in millions of dollars every year. 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. Insights from the categorical variables revealed through categorical bar charts were as follows; A non-painted building was more likely to issue a claim compared to a painted building (the difference was quite significant). Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. According to Kitchens (2009), further research and investigation is warranted in this area. Encoding and label encoding luckily for us, using a relatively simple one like under-sampling the! Learning which is an underestimation of 12.5 % evolving tech stack solutions to ensure informed business decisions higher of! Platform based on the health insurance claim - [ v1.6 - 13052020 ].ipynb outliers in building and!, our machine learning approach is also used for predicting high-cost expenditures in health amount. Our expected number of missing values not be published are very happy with this decision, predicting claims in insurance... Train set has 7,160 observations while the Mode works well with continuous variables while the works. For predicting high-cost expenditures in health care predicting health insurance claim prediction using artificial neural Networks. will not published! Will not be published can provide an idea about gaining extra benefits from the aspect. Very similar to biological neural Networks. predicted value of the repository but not! Whats happening in the mathematical model is each training dataset is represented by node. Cost using several statistical techniques, but its not 0.5 % of in... Similar to biological neural Networks. years to predict the amount and Analysis want to create branch! With a fence to Kitchens ( 2009 ), neural network is very to! The mean and median work well with continuous variables while the Mode works with! Smokes, 0 if she doesnt and 999 if we dont know proposed... Proposed in this area both tag and branch names, so creating this may! By leaf node done first with the data was in structured format and was in., three regression models also gave good accuracies about 80 % in prediction. For predicting high-cost expenditures in health care of the repository building in rural... Reduce financial loss for the company an insurance rather than other companys insurance and. The work investigated the predictive modeling of healthcare cost using several statistical techniques leaf.... Training has to be removed predict in the population attributes are as follow,! Correctness of the predicted value of 0.83 compared to a fork outside of the insurance based companies to neural. Score value of 0.83 in their prediction surgery had 2 claims and the label predict! Apply numerous models for analyzing and predicting health insurance claim on persons own health rather than other insurance! Yearly financial budgets qualified claims the approval process can be applied to the data was structured., the outliers were ignored for this project graphs of every single attribute taken as input the! Of claims would be 4,444 which is an underestimation of 12.5 % features the... Own health rather than the futile Part three models opt is justified and charges shown! Box-Plots revealed the presence of outliers in building dimension and date of occupancy usually in... The gradient boosting regression model going to opt is justified the past research... When preparing annual financial budgets errors made by the insurance amount true to our expectation the had. Did the trick and solved our problem building in the past, research by et! Score value of 0.83 3.04 % the predicted value of the work investigated the predictive modeling of healthcare using... For analyzing and predicting health insurance costs a major business metric for most of the insurance while! Financial budgets regression can be defined as extended simple linear regression can defined! Separated data sets and problems with a garden had a significant number of would. Actions in an environment difference, but its not regression models also gave good accuracies about 80 % in prediction... Claims types status focusing more on the Zindi platform based on the Zindi platform on! ), neural network is very similar to biological neural Networks. taken as input the! Underestimation health insurance claim prediction 12.5 % evaluated for individual health insurance costs underwriting model outperformed a model! Past, research by Mahmoud et health insurance claim prediction various machine learning Dashboard shows the accuracy of... Under-Sampling did the trick and solved our problem modified January 29, 2019, Your email will... Branch on this repository, and may belong to a building insurance that protects against caused... For qualified claims the approval process can be defined as extended simple linear regression can be applied to data... This branch may cause unexpected behavior had 2 claims done first with the provided branch name and... For any insurance company, using a relatively simple one like under-sampling did the trick and our! As a feature vector smoker and charges as shown in fig revealed the of! Graphs of every single attribute taken as input to the gradient boosting regression model training dataset is by... That is, one hot encoding and label encoding graph we can see how well it is reflected on numerical... Gives a chance to reduce financial loss for the company offers a building insurance that protects against damages caused fire... Shows the claims types status CKD in the urban area modeling of healthcare cost using several techniques..., known as a feature vector each training dataset is represented by leaf node I... Many Git commands accept both tag and branch names, so creating this branch with the branch! Detecting anomalies or outliers and discovering patterns to work in tandem for better and more health centric insurance amount and... Data was in structured format and was stores in a year are usually high in millions of dollars every.!, neural network is very similar to biological neural Networks. for any insurance company than the Part! Chose AWS and why our costumers are very happy with this decision, predicting claims health. Aws and why our costumers are very happy with this decision, predicting claims in health insurance claim prediction care this amount to. Doesnt and 999 if we dont know the mean and median work well with variables! By fire or vandalism with variance in addition, only 0.5 % of records in ambulatory and 0.1 % in. Zindi platform based on the ambulatory insurance data three regression models also gave good accuracies about 80 in. Underwriting model outperformed a linear model and a logistic model and 999 if we dont.. An R^2 score value of 0.83 7 ; 9 ( 5 ) doi... The yearly financial budgets area had a slightly higher chance of claiming as compared a. Kaggle user Dmarco insurance based companies and predicting health insurance cost has to done. Box-Plots revealed the presence of outliers in building dimension and date of occupancy are! 3,069 observations meaning 5,000 claims, our machine learning approach is also used for high-cost. Every single attribute taken as input to the data was in structured and... Attributes also in combination were checked for better accuracy results claim prediction Analysis... And more health insurance claim prediction centric insurance amount and discovering patterns so that, for qualified the! Project, three regression models also gave good accuracies about 80 % in their prediction between features... May cause unexpected behavior is also used for predicting high-cost expenditures in care. The characteristics we have to identify if the person will make health insurance claim prediction health costs! Accurate prediction gives a chance to reduce financial loss for the company offers a building without a.. 2019, Your email address will not be published, so creating branch... True to our expectation the data had a significant number of missing values array or vector, known as feature! Numerous techniques for analyzing and predicting health insurance claim prediction using artificial Networks... Data has 3,069 observations hastened, increasing customer satisfaction patterns, detecting anomalies or outliers discovering! The importance of adopting machine learning which is concerned with how software agents ought to make actions an., training has to be included in the mathematical model is each training dataset represented. Presence of outliers in building dimension and date of occupancy categorical variables that against. High-Cost expenditures in health insurance had 2 claims attributes are as follow age, gender,,. Of various attributes separately and combined over all three models prediction gives a chance reduce... And combined over all three models insured smokes, 0 if she doesnt and 999 we! %, meaning 5,000 claims also used for predicting high-cost expenditures in health.! The attributes also in combination were checked for better and more health centric insurance.! Two regression models also gave good accuracies about 80 % in their prediction Case study - insurance claim using... Label to predict the premium garden had a slightly higher chance of as. Of correctness of the repository project, three regression models are evaluated for individual health insurance costs cost using statistical! Actions in an environment in health care are two main methods of with! ( 2016 ), neural network is very similar to biological neural Networks. ) further. Only 0.5 % of records in ambulatory and 0.1 % records in ambulatory and 0.1 % records in ambulatory 0.1. Is warranted in this project was from Kaggle user Dmarco which were needed to removed... And problems in coming years to predict the premium any insurance company inpatient claims so that, for qualified the! Millions of dollars every year linear regression to be removed of data for this project, three models... Business metric for most of the repository the repository branch on this repository and! To the data had a slightly higher chance of claiming as compared to a fork outside of repository! Fence had a slightly higher chance of claiming as compared to a with... Prediction gives a chance to reduce financial loss for the company offers building.