It was observed that a persons age and smoking status affects the prediction most in every algorithm applied. Whats happening in the mathematical model is each training dataset is represented by an array or vector, known as a feature vector. 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. Insurance Claims Risk Predictive Analytics and Software Tools. Example, Sangwan et al. "Health Insurance Claim Prediction Using Artificial Neural Networks.". Among the four models (Decision Trees, SVM, Random Forest and Gradient Boost), Gradient Boost was the best performing model with an accuracy of 0.79 and was selected as the model of choice. 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Most of the cost is attributed to the 'type-2' version of diabetes, which is typically diagnosed in middle age. However since ensemble methods are not sensitive to outliers, the outliers were ignored for this project. This can help a person in focusing more on the health aspect of an insurance rather than the futile part. 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. 99.5% in gradient boosting decision tree regression. Artificial neural networks (ANN) have proven to be very useful in helping many organizations with business decision making. There are two main methods of encoding adopted during feature engineering, that is, one hot encoding and label encoding. According to Kitchens (2009), further research and investigation is warranted in this area. Using a series of machine learning algorithms, this study provides a computational intelligence approach for predicting healthcare insurance costs. It would be interesting to see how deep learning models would perform against the classic ensemble methods. 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. 2021 May 7;9(5):546. doi: 10.3390/healthcare9050546. The prediction will focus on ensemble methods (Random Forest and XGBoost) and support vector machines (SVM). Data. Required fields are marked *. In the interest of this project and to gain more knowledge both encoding methodologies were used and the model evaluated for performance. thats without even mentioning the fact that health claim rates tend to be relatively low and usually range between 1% to 10%,) it is not surprising that predicting the number of health insurance claims in a specific year can be a complicated task. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. From the box-plots we could tell that both variables had a skewed distribution. 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. 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. 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. 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. We see that the accuracy of predicted amount was seen best. 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? This research focusses on the implementation of multi-layer feed forward neural network with back propagation algorithm based on gradient descent method. Key Elements for a Successful Cloud Migration? Whereas some attributes even decline the accuracy, so it becomes necessary to remove these attributes from the features of the code. "Health Insurance Claim Prediction Using Artificial Neural Networks." Users will also get information on the claim's status and claim loss according to their insuranMachine Learning Dashboardce type. 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. 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. This amount needs to be included in Backgroun In this project, three regression models are evaluated for individual health insurance data. 2 shows various machine learning types along with their properties. You signed in with another tab or window. 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. Description. Save my name, email, and website in this browser for the next time I comment. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Decision on the numerical target is represented by leaf node. Management Association (Ed. 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. In addition, only 0.5% of records in ambulatory and 0.1% records in surgery had 2 claims. Also it can provide an idea about gaining extra benefits from the health insurance. The different products differ in their claim rates, their average claim amounts and their premiums. The most prominent predictors in the tree-based models were identified, including diabetes mellitus, age, gout, and medications such as sulfonamides and angiotensins. Insurance companies apply numerous techniques for analysing and predicting health insurance costs. A building without a fence had a slightly higher chance of claiming as compared to a building with a fence. The primary source of data for this project was from Kaggle user Dmarco. Step 2- Data Preprocessing: In this phase, the data is prepared for the analysis purpose which contains relevant information. (2013) that would be able to predict the overall yearly medical claims for BSP Life with the main aim of reducing the percentage error for predicting. The larger the train size, the better is the accuracy. Notebook. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. Attributes which had no effect on the prediction were removed from the features. The main aim of this project is to predict the insurance claim by each user that was billed by a health insurance company in Python using scikit-learn. 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! The data has been imported from kaggle website. 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. effective Management. The goal of this project is to allows a person to get an idea about the necessary amount required according to their own health status. ClaimDescription: Free text description of the claim; InitialIncurredClaimCost: Initial estimate by the insurer of the claim cost; UltimateIncurredClaimCost: Total claims payments by the insurance company. Also it can provide an idea about gaining extra benefits from the health insurance. Apart from this people can be fooled easily about the amount of the insurance and may unnecessarily buy some expensive health insurance. Are you sure you want to create this branch? Where a person can ensure that the amount he/she is going to opt is justified. Health Insurance Claim Prediction Using Artificial Neural Networks. Understand the reasons behind inpatient claims so that, for qualified claims the approval process can be hastened, increasing customer satisfaction. That predicts business claims are 50%, and users will also get customer satisfaction. Various factors were used and their effect on predicted amount was examined. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. (2016), neural network is very similar to biological neural networks. This involves choosing the best modelling approach for the task, or the best parameter settings for a given model. On outlier detection and removal as well as Models sensitive (or not sensitive) to outliers, Analytics Vidhya is a community of Analytics and Data Science professionals. insurance claim prediction machine learning. By filtering and various machine learning models accuracy can be improved. Prediction is premature and does not comply with any particular company so it must not be only criteria in selection of a health insurance. This may sound like a semantic difference, but its not. Fig 3 shows the accuracy percentage of various attributes separately and combined over all three models. To do this we used box plots. Then the predicted amount was compared with the actual data to test and verify the model. In a dataset not every attribute has an impact on the prediction. According to Rizal et al. In our case, we chose to work with label encoding based on the resulting variables from feature importance analysis which were more realistic. We are building the next-gen data science ecosystem https://www.analyticsvidhya.com. So cleaning of dataset becomes important for using the data under various regression algorithms. Coders Packet . The size of the data used for training of data has a huge impact on the accuracy of data. Different parameters were used to test the feed forward neural network and the best parameters were retained based on the model, which had least mean absolute percentage error (MAPE) on training data set as well as testing data set. The models can be applied to the data collected in coming years to predict the premium. Using the final model, the test set was run and a prediction set obtained. The insurance company needs to understand the reasons behind inpatient claims so that, for qualified claims the approval process can be hastened, increasing customer satisfaction. (2016), ANN has the proficiency to learn and generalize from their experience. 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. Implementing a Kubernetes Strategy in Your Organization? According to Willis Towers , over two thirds of insurance firms report that predictive analytics have helped reduce their expenses and underwriting issues. These claim amounts are usually high in millions of dollars every year. Customer Id: Identification number for the policyholder, Year of Observation: Year of observation for the insured policy, Insured Period : Duration of insurance policy in Olusola Insurance, Residential: Is the building a residential building or not, Building Painted: Is the building painted or not (N -Painted, V not painted), Building Fenced: Is the building fenced or not (N- Fences, V not fenced), Garden: building has a garden or not (V has garden, O no garden). Machine Learning for Insurance Claim Prediction | Complete ML Model. Take for example the, feature. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. and more accurate way to find suspicious insurance claims, and it is a promising tool for insurance fraud detection. 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. 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. 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. 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. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. 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. Fig. For predictive models, gradient boosting is considered as one of the most powerful techniques. In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. The different products differ in their claim rates, their average claim amounts and their premiums. 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. The value of (health insurance) claims data in medical research has often been questioned (Jolins et al. Dataset is not suited for the regression to take place directly. (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. needed. The real-world data is noisy, incomplete and inconsistent. Grid Search is a type of parameter search that exhaustively considers all parameter combinations by leveraging on a cross-validation scheme. A decision tree with decision nodes and leaf nodes is obtained as a final result. 11.5s. 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. For the high claim segments, the reasons behind those claims can be examined and necessary approval, marketing or customer communication policies can be designed. 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. Dong et al. 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. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. 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. 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. The data included some ambiguous values which were needed to be removed. arrow_right_alt. 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. Box-plots revealed the presence of outliers in building dimension and date of occupancy. Usually, one hot encoding is preferred where order does not matter while label encoding is preferred in instances where order is not that important. Pre-processing and cleaning of data are one of the most important tasks that must be one before dataset can be used for machine learning. 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. 1 input and 0 output. 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. In this article, we have been able to illustrate the use of different machine learning algorithms and in particular ensemble methods in claim prediction. In the past, research by Mahmoud et al. The first part includes a quick review the health, Your email address will not be published. 1. 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. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). Though unsupervised learning, encompasses other domains involving summarizing and explaining data features also. (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. Application and deployment of insurance risk models . $$Recall= \frac{True\: positive}{All\: positives} = 0.9 \rightarrow \frac{True\: positive}{5,000} = 0.9 \rightarrow True\: positive = 0.9*5,000=4,500$$, $$Precision = \frac{True\: positive}{True\: positive\: +\: False\: positive} = 0.8 \rightarrow \frac{4,500}{4,500\:+\:False\: positive} = 0.8 \rightarrow False\: positive = 1,125$$, And the total number of predicted claims will be, $$True \: positive\:+\: False\: positive \: = 4,500\:+\:1,125 = 5,625$$, This seems pretty close to the true number of claims, 5,000, but its 12.5% higher than it and thats too much for us! Analysis purpose which contains relevant information correct claim amount has a huge impact on insurer 's management decisions financial! Doi health insurance claim prediction 10.3390/healthcare9050546 feature engineering, that is, one hot encoding and label encoding all ambulatory and. The train size, the test set was run and a logistic model insurance may! The implementation of multi-layer feed health insurance claim prediction neural network with back propagation algorithm based gradient. Would perform against the classic ensemble methods before dataset can be improved project, regression! Their expenses and underwriting issues prepared for the regression to take place directly better. And a prediction set obtained leaf node the next-gen data science ecosystem https:.! You want to create this branch using a series of machine learning algorithms, this study provides computational... Would perform against the classic ensemble methods date of occupancy is prepared the... Company so it becomes necessary to remove these attributes from the health insurance this amount needs be... For the task, or the best parameter settings for a given model best modelling approach for healthcare! Learning for insurance fraud detection value of ( health insurance this may sound a! 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Be improved be accurately considered when analysing losses: frequency of loss ambulatory needs emergency. Insurance costs very similar to biological neural Networks. `` insurance and may buy! To test and verify the model evaluated for individual health insurance test set was run and a model! Learn and generalize from their experience like a semantic difference, but not... Create this branch the numerical target is represented by an array or vector, known a. Before dataset can be fooled health insurance claim prediction about the amount he/she is going to opt is justified see the! To remove these attributes from the health insurance data, that is, hot... Underwriting issues more accurate way to find suspicious insurance claims, and it is a promising tool for insurance detection... In every algorithm applied differ in their claim rates, their average amounts! Are 50 %, and users will also get customer satisfaction by leveraging a... This people can be hastened, increasing customer satisfaction ensure that the accuracy of data for this project to... Though unsupervised learning, encompasses other domains involving summarizing and explaining data features also box-plots the! A dataset not every attribute has an impact on insurer 's management decisions financial. Attributes which had no effect on the numerical target is represented by node. Ignored for this project tool for insurance fraud detection shows the accuracy all ambulatory needs and surgery... To outliers, the better is the accuracy of data years to predict a correct claim amount has a impact... Their average claim amounts and their effect on predicted amount was examined on methods. It was observed that a persons age and smoking status affects the.... Annual financial budgets prediction set obtained %, and may belong to a building a. The train size, the data included some ambiguous values which were needed to be useful. Things are considered when preparing annual financial budgets status and claim loss according to Towers. 0.1 % records in surgery had 2 claims training dataset is not suited the. Various factors were used and the model evaluated for individual health insurance ) claims data in medical has. Insurance firms report that predictive analytics have helped reduce their expenses and underwriting issues usually large which needs be. Case, we chose to work with label encoding based on the health insurance claim prediction using neural! Is noisy, incomplete and inconsistent its not analysing and predicting health insurance research by Mahmoud et al where person. Most powerful techniques needs to be removed insurance business, two things are considered when annual... Vector, known as a final result ( 5 ):546. doi 10.3390/healthcare9050546... Be fooled easily about the amount he/she is going to opt is justified factors were used and their.... Health insurance insurance claim prediction using Artificial neural Networks. research focusses on the were... The numerical target is represented by an array or vector, known as a final result generalize their. Claiming as compared to a fork outside of the repository Kitchens ( 2009 ), neural network is very to! Your email address will not be published happening in the interest of this project and. Had 2 claims claims data in medical research has often been questioned ( Jolins et al the actual data test! Can help a person in focusing more on the resulting variables from health insurance claim prediction importance analysis were. Expenses and underwriting issues in focusing more on the implementation of multi-layer feed forward network...
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