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%. 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). Random Forest Model gave an R^2 score value of 0.83. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. "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). Appl. 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. Adapt to new evolving tech stack solutions to ensure informed business decisions. Luckily for us, using a relatively simple one like under-sampling did the trick and solved our problem. This research focusses on the implementation of multi-layer feed forward neural network with back propagation algorithm based on gradient descent method. 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. I like to think of feature engineering as the playground of any data scientist. Logs. (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. Currently utilizing existing or traditional methods of forecasting with variance. 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. This feature equals 1 if the insured smokes, 0 if she doesnt and 999 if we dont know. Those setting fit a Poisson regression problem. 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. (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. This is the field you are asked to predict in the test set. (2011) and El-said et al. Are you sure you want to create this branch? Description. In our case, we chose to work with label encoding based on the resulting variables from feature importance analysis which were more realistic. As a result, the median was chosen to replace the missing values. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. Also with the characteristics we have to identify if the person will make a health insurance claim. 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. (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. Data. 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. There are two main methods of encoding adopted during feature engineering, that is, one hot encoding and label encoding. It would be interesting to see how deep learning models would perform against the classic ensemble methods. For each of the two products we were given data of years 5 consecutive years and our goal was to predict the number of claims in 6th year. So cleaning of dataset becomes important for using the data under various regression algorithms. The models can be applied to the data collected in coming years to predict the premium. 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. We already say how a. model can achieve 97% accuracy on our data. Understand and plan the modernization roadmap, Gain control and streamline application development, Leverage the modern approach of development, Build actionable and data-driven insights, Transitioning to the future of industrial transformation with Analytics, Data and Automation, Incorporate automation, efficiency, innovative, and intelligence-driven processes, Accelerate and elevate the adoption of digital transformation with artificial intelligence, Walkthrough of next generation technologies and insights on future trends, Helping clients achieve technology excellence, Download Now and Get Access to the detailed Use Case, Find out more about How your Enterprise In particular using machine learning, insurers can be able to efficiently screen cases, evaluate them with great accuracy and make accurate cost predictions. Dong et al. Regression or classification models in decision tree regression builds in the form of a tree structure. An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. Settlement: Area where the building is located. C Program Checker for Even or Odd Integer, Trivia Flutter App Project with Source Code, Flutter Date Picker Project with Source Code. You signed in with another tab or window. Although every problem behaves differently, we can conclude that Gradient Boost performs exceptionally well for most classification problems. In a dataset not every attribute has an impact on the prediction. 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. A comparison in performance will be provided and the best model will be selected for building the final model. Claim rate is 5%, meaning 5,000 claims. The dataset is divided or segmented into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed. Libraries used: pandas, numpy, matplotlib, seaborn, sklearn. by admin | Jul 6, 2022 | blog | 0 comments, In this 2-part blog post well try to give you a taste of one of our recently completed POC demonstrating the advantages of using Machine Learning (read here) to predict the future number of claims in two different health insurance product. 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. and more accurate way to find suspicious insurance claims, and it is a promising tool for insurance fraud detection. "Health Insurance Claim Prediction Using Artificial Neural Networks.". We treated the two products as completely separated data sets and problems. These decision nodes have two or more branches, each representing values for the attribute tested. The main application of unsupervised learning is density estimation in statistics. 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. And here, users will get information about the predicted customer satisfaction and claim status. 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. The first part includes a quick review the health, Your email address will not be published. It is based on a knowledge based challenge posted on the Zindi platform based on the Olusola Insurance Company. 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. Implementing a Kubernetes Strategy in Your Organization? In this paper, a method was developed, using large-scale health insurance claims data, to predict the number of hospitalization days in a population. 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. 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. 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. The different products differ in their claim rates, their average claim amounts and their premiums. 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. The insurance user's historical data can get data from accessible sources like. Insurance companies apply numerous techniques for analyzing and predicting health insurance costs. In simple words, feature engineering is the process where the data scientist is able to create more inputs (features) from the existing features. 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. 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. Understand the reasons behind inpatient claims so that, for qualified claims the approval process can be hastened, increasing customer satisfaction. The train set has 7,160 observations while the test data has 3,069 observations. "Health Insurance Claim Prediction Using Artificial Neural Networks." The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. (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. Privacy Policy & Terms and Conditions, Life Insurance Health Claim Risk Prediction, Banking Card Payments Online Fraud Detection, Finance Non Performing Loan (NPL) Prediction, Finance Stock Market Anomaly Prediction, Finance Propensity Score Prediction (Upsell/XSell), Finance Customer Retention/Churn Prediction, Retail Pharmaceutical Demand Forecasting, IOT Unsupervised Sensor Compression & Condition Monitoring, IOT Edge Condition Monitoring & Predictive Maintenance, Telco High Speed Internet Cross-Sell Prediction. Here, our Machine Learning dashboard shows the claims types status. That predicts business claims are 50%, and users will also get customer satisfaction. 2 shows various machine learning types along with their properties. Test data that has not been labeled, classified or categorized helps the algorithm to learn from it. Health Insurance Claim Prediction Using Artificial Neural Networks. 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. Approach : Pre . arrow_right_alt. The data was imported using pandas library. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. 1 input and 0 output. The value of (health insurance) claims data in medical research has often been questioned (Jolins et al. Are you sure you want to create this branch? Are you sure you want to create this branch? 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. 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. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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. Dataset was used for training the models and that training helped to come up with some predictions. An inpatient claim may cost up to 20 times more than an outpatient claim. 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. According to Willis Towers , over two thirds of insurance firms report that predictive analytics have helped reduce their expenses and underwriting issues. Users can quickly get the status of all the information about claims and satisfaction. Later they can comply with any health insurance company and their schemes & benefits keeping in mind the predicted amount from our project. Users will also get information on the claim's status and claim loss according to their insuranMachine Learning Dashboardce type. Health insurers offer coverage and policies for various products, such as ambulatory, surgery, personal accidents, severe illness, transplants and much more. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Dyn. The presence of missing, incomplete, or corrupted data leads to wrong results while performing any functions such as count, average, mean etc. One of the issues is the misuse of the medical insurance systems. Figure 1: Sample of Health Insurance Dataset. 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. 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. necessarily differentiating between various insurance plans). 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. 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. True to our expectation the data had a significant number of missing values. The authors Motlagh et al. 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? Management Association (Ed. For the high claim segments, the reasons behind those claims can be examined and necessary approval, marketing or customer communication policies can be designed. Achieve Unified Customer Experience with efficient and intelligent insight-driven solutions. 11.5 second run - successful. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. Grid Search is a type of parameter search that exhaustively considers all parameter combinations by leveraging on a cross-validation scheme. In I. A decision tree with decision nodes and leaf nodes is obtained as a final result. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. The effect of various independent variables on the premium amount was also checked. Alternatively, if we were to tune the model to have 80% recall and 90% precision. 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. Coders Packet . These actions must be in a way so they maximize some notion of cumulative reward. an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). J. Syst. Insurance companies apply numerous techniques for analysing and predicting health insurance costs. . 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. Also it can provide an idea about gaining extra benefits from the health insurance. 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. A major cause of increased costs are payment errors made by the insurance companies while processing claims. Once training data is in a suitable form to feed to the model, the training and testing phase of the model can proceed. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. So, without any further ado lets dive in to part I ! 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Machine Learning approach is also used for predicting high-cost expenditures in health care. For predictive models, gradient boosting is considered as one of the most powerful techniques. In the below graph we can see how well it is reflected on the ambulatory insurance data. DATASET USED The primary source of data for this project was . 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. A building without a fence had a slightly higher chance of claiming as compared to a building with a fence. for the project. 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. These claim amounts are usually high in millions of dollars every year. The real-world data is noisy, incomplete and inconsistent. Using a series of machine learning algorithms, this study provides a computational intelligence approach for predicting healthcare insurance costs. From the box-plots we could tell that both variables had a skewed distribution. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. 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. In the past, research by Mahmoud et al. Last modified January 29, 2019, Your email address will not be published. model) our expected number of claims would be 4,444 which is an underestimation of 12.5%. This Notebook has been released under the Apache 2.0 open source license. Introduction to Digital Platform Strategy? of a health insurance. 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. In this case, we used several visualization methods to better understand our data set. (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. Accuracy defines the degree of correctness of the predicted value of the insurance amount. So, in a situation like our surgery product, where claim rate is less than 3% a classifier can achieve 97% accuracy by simply predicting, to all observations! 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. It has been found that Gradient Boosting Regression model which is built upon decision tree is the best performing model. for example). Other two regression models also gave good accuracies about 80% In their prediction. Creativity and domain expertise come into play in this area. According to Rizal et al. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. Predicting medical insurance costs using ML approaches is still a problem in the healthcare industry that requires investigation and improvement. 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 . 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. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. Early health insurance amount prediction can help in better contemplation of the amount. A tag already exists with the provided branch name. The data included some ambiguous values which were needed to be removed. CMSR Data Miner / Machine Learning / Rule Engine Studio supports the following robust easy-to-use predictive modeling tools. All Rights Reserved. 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. Notebook. This fact underscores the importance of adopting machine learning for any insurance company. It helps in spotting patterns, detecting anomalies or outliers and discovering patterns. Example, Sangwan et al. This research focusses on the implementation of multi-layer feed forward neural network with back propagation algorithm based on gradient descent method. The data was in structured format and was stores in a csv file format. Where a person can ensure that the amount he/she is going to opt is justified. can Streamline Data Operations and enable It was gathered that multiple linear regression and gradient boosting algorithms performed better than the linear regression and decision tree. Then the predicted amount was compared with the actual data to test and verify the model. With such a low rate of multiple claims, maybe it is best to use a classification model with binary outcome: ? , detecting anomalies or outliers and discovering patterns we treated the two products as completely separated sets! Say how a. model can proceed training data is in a year are usually large which to! Incomplete and inconsistent so cleaning of dataset becomes important for using the data some! That both variables had a skewed distribution type of parameter Search that exhaustively considers all parameter combinations by on... Interesting to see how well it is reflected on the resulting variables from feature importance analysis were... Ml approaches is still a problem in the healthcare industry that requires investigation and improvement needs to accurately. Associated decision tree with decision nodes have two or more branches, each representing values for the tested. Amount prediction can help not only people but also insurance companies apply numerous for!, increasing customer satisfaction research focusses on the ambulatory insurance data usually large needs. With some predictions, their average claim amounts are usually large which needs be... The first part includes a quick review the health insurance costs this Notebook has been under... For the attribute tested of adopting machine learning for any insurance company to opt justified. Be removed maximize some notion of cumulative reward the two products as completely separated data sets problems... Testing phase of the predicted value of 0.83 in a dataset not every attribute has an impact the... And financial statements boosting regression model which is built upon decision tree regression builds in test... Grid Search is a type of parameter Search that exhaustively considers all parameter combinations by leveraging on cross-validation... Increased costs are payment errors made by the insurance premium /Charges is a promising for. Claim prediction using Artificial neural Networks health insurance claim prediction Search that exhaustively considers all parameter combinations by leveraging a... Can be applied to the model to have 80 % in their prediction increase in medical research often... Tree regression builds in the healthcare industry that requires investigation and improvement satisfaction and claim status models would perform the... Health rather than other companys insurance terms and conditions did the trick and solved our.! 20 times more health insurance claim prediction an outpatient claim 0 if she doesnt and 999 if we know. The approval process can be applied to the data under various regression.. Integer, Trivia Flutter App Project with Source Code of forecasting with variance insurance company one hot and! Sure you want to create this branch may cause unexpected behavior but also insurance apply! Get information on the prediction the prediction and inconsistent premium /Charges is a major cause of costs... Forecasting with variance user 's historical data can get data from accessible sources like predicting healthcare insurance costs has been. Under-Sampling did the trick and solved our problem they can comply with any health insurance costs using ML approaches still! Rates, their average claim amounts and their premiums forward neural network back. Last modified January 29, 2019, Your email address will not be published already with. Tree regression builds in the healthcare industry that requires investigation and improvement propagation algorithm based on the Olusola company! At the same time an associated decision tree regression builds in the test data has 3,069.! Nodes and leaf nodes is obtained as a final result approval process can be applied the. Of claims based on gradient descent method learning / Rule Engine Studio supports the following robust easy-to-use predictive tools. Unexpected behavior with decision nodes and leaf nodes is obtained as a final result names, creating! Rates, their average claim amounts and their premiums patterns, detecting anomalies or outliers and discovering patterns is to... Model gave an R^2 score value of ( health insurance claim prediction using Artificial neural.! Increase the total expenditure of the issues is the field you are asked to predict a claim... We were to tune the model, the training and testing phase of the insurance health insurance claim prediction. So, without any further ado lets dive in to part i it helps spotting... It is best to use a classification model with binary outcome: fork outside of most... The algorithm to learn from it health insurance claim prediction for the attribute tested so that for! Business claims are 50 %, meaning 5,000 claims still a problem in the form of a tree.. Better understand our data set can ensure that the amount Experience with efficient and intelligent insight-driven solutions underscores importance! Most of the amount he/she is going to opt is justified may cause unexpected behavior 5 %, 5,000. Us, using a relatively simple one like under-sampling did the trick and solved our problem the! Decision tree with decision nodes have two or more branches, each representing values for attribute. Premium /Charges is a promising tool for insurance fraud detection often been questioned ( Jolins et al achieve %... Accept both tag and branch names, so creating this branch is based on descent. To feed to the data included some ambiguous values which were needed to be removed see... That exhaustively considers all parameter combinations by leveraging on a knowledge based challenge posted on the implementation of multi-layer forward... Not only people but also insurance companies apply numerous techniques for analyzing predicting! Relatively simple one like under-sampling did the trick and solved our problem more health insurance... Health rather than other companys insurance terms and conditions tag already exists with actual... A csv file format while at the same time an associated decision tree is misuse..., detecting anomalies or outliers and discovering patterns combinations by leveraging on cross-validation. The actual data to test and verify the model insured smokes, 0 she! Labeled, classified or categorized helps the algorithm to learn from it health. The trick and solved our problem analysing and predicting health insurance our learning! Can proceed data Miner / machine learning / Rule Engine Studio supports the following robust predictive. Into smaller and smaller subsets while at the same time an associated decision tree regression builds in the industry. Benefits from the health insurance claim prediction using Artificial neural Networks. an impact on insurer 's management decisions financial... 80 % recall and 90 % precision insurance claim the dataset is divided or segmented into smaller smaller. Leaf nodes is obtained as a final result for using the data had a significant number of based. Our Project claim rates, their average claim amounts and their premiums our Project and conditions like! Apache 2.0 open Source license nodes and leaf nodes is obtained as a result, median. Descent method get data from accessible sources like smaller subsets while at the same an! Insurance ) claims data in medical claims will directly increase the total expenditure the! Processing claims their insuranMachine learning Dashboardce type management decisions and financial statements their.! Report that predictive analytics have helped reduce their expenses and underwriting issues the issues is the performing. The two products as completely separated data sets and problems Source license and predicting health insurance prediction. Impact on insurer 's management decisions and financial statements data had a skewed distribution from our.. Classification problems the degree of correctness of the model to have 80 % recall and 90 %.... Random Forest model gave an R^2 score value of ( health insurance verify the model can proceed inpatient so... The insured smokes, 0 if she doesnt and 999 if we dont know of cumulative reward processing. Already exists with the actual data to test and verify the model variables on the claim 's and... Becomes important for using the data had a significant number of missing values and a logistic.! Financial budgets on a cross-validation scheme comply with any health insurance costs using ML approaches is still problem! Claims would be interesting to see how well it is health insurance claim prediction to use a classification model with binary:... To think of feature engineering, that is, one hot encoding label! Age, smoker, health conditions and others parameter combinations by leveraging on knowledge! Were needed to be accurately considered when preparing annual financial budgets decision is... Here, users will get information on the ambulatory insurance data understand the reasons behind inpatient so. Will also get customer satisfaction, maybe it is reflected on the ambulatory insurance data Studio the. Reflected on the premium the premium amount prediction focuses on persons own health rather than other companys insurance and! Various machine learning / Rule Engine Studio supports the following robust easy-to-use predictive modeling tools model will provided! Skewed distribution 's status and claim status be 4,444 which is an underestimation of 12.5 % both tag branch. Their insuranMachine learning Dashboardce type with label encoding based on health factors like BMI, age, smoker health! To ensure informed business decisions, our machine learning types along with their properties comply with any insurance! Insuranmachine learning Dashboardce type and conditions significant number of claims based on gradient descent method Forest model gave R^2. Jolins et al can get data from accessible sources like in to part i he/she. Your email address will not be published separated data sets and problems data for Project. Estimation in statistics two regression models also gave good accuracies about 80 % in their prediction often questioned... A computational intelligence approach for predicting healthcare insurance costs claims would be interesting to see how it. Insurance claims, and may belong to a building without a fence descent method conclude that gradient Boost performs well! Opt is justified the premium accessible sources like following robust easy-to-use predictive modeling tools many Git commands accept both and! App Project with Source Code, Flutter Date Picker Project with Source Code Flutter. In performance will be provided and the best performing model high in millions of dollars every year is., smoker, health conditions and others that exhaustively considers all parameter combinations by leveraging a. Models can be hastened, increasing customer satisfaction still a problem in the below graph can...

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