It is based on a knowledge based challenge posted on the Zindi platform based on the Olusola Insurance Company. Gradient boosting involves three elements: An additive model to add weak learners to minimize the loss function. It was gathered that multiple linear regression and gradient boosting algorithms performed better than the linear regression and decision tree. This amount needs to be included in the yearly financial budgets. 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 . 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Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Currently utilizing existing or traditional methods of forecasting with variance. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. ANN has the ability to resemble the basic processes of humans behaviour which can also solve nonlinear matters, with this feature Artificial Neural Network is widely used with complicated system for computations and classifications, and has cultivated on non-linearity mapped effect if compared with traditional calculating methods. An inpatient claim may cost up to 20 times more than an outpatient claim. history Version 2 of 2. 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. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. The dataset is divided or segmented into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed. 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. This is the field you are asked to predict in the test set. Where a person can ensure that the amount he/she is going to opt is justified. 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. Also it can provide an idea about gaining extra benefits from the health insurance. Fig. of a health insurance. These actions must be in a way so they maximize some notion of cumulative reward. 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. 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. 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. 1. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. That predicts business claims are 50%, and users will also get customer satisfaction. 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? Fig 3 shows the accuracy percentage of various attributes separately and combined over all three models. 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. This algorithm for Boosting Trees came from the application of boosting methods to regression trees. The model predicted the accuracy of model by using different algorithms, different features and different train test split size. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. 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. Test data that has not been labeled, classified or categorized helps the algorithm to learn from it. Comments (7) Run. Abhigna et al. Logs. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). Notebook. The distribution of number of claims is: Both data sets have over 25 potential features. HEALTH_INSURANCE_CLAIM_PREDICTION. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. for example). 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. Grid Search is a type of parameter search that exhaustively considers all parameter combinations by leveraging on a cross-validation scheme. 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. Model performance was compared using k-fold cross validation. "Health Insurance Claim Prediction Using Artificial Neural Networks." Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. 2 shows various machine learning types along with their properties. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. Apart from this people can be fooled easily about the amount of the insurance and may unnecessarily buy some expensive health insurance. Reinforcement learning is class of machine learning which is concerned with how software agents ought to make actions in an environment. These inconsistencies must be removed before doing any analysis on data. The models can be applied to the data collected in coming years to predict the premium. In addition, only 0.5% of records in ambulatory and 0.1% records in surgery had 2 claims. These decision nodes have two or more branches, each representing values for the attribute tested. 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. Using a series of machine learning algorithms, this study provides a computational intelligence approach for predicting healthcare insurance costs. 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. Box-plots revealed the presence of outliers in building dimension and date of occupancy. 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. The attributes also in combination were checked for better accuracy results. Refresh the page, check. A building without a fence had a slightly higher chance of claiming as compared to a building with a fence. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. 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. The increasing trend is very clear, and this is what makes the age feature a good predictive feature. The train set has 7,160 observations while the test data has 3,069 observations. arrow_right_alt. C Program Checker for Even or Odd Integer, Trivia Flutter App Project with Source Code, Flutter Date Picker Project with Source Code. (2011) and El-said et al. Dyn. 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). Data. All Rights Reserved. The larger the train size, the better is the accuracy. 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. Adapt to new evolving tech stack solutions to ensure informed business decisions. Attributes are as follow age, gender, bmi, children, smoker and charges as shown in Fig. Health Insurance Cost Predicition. Factors determining the amount of insurance vary from company to company. The building dimension and date of occupancy being continuous in nature, we needed to understand the underlying distribution. You signed in with another tab or window. These claim amounts are usually high in millions of dollars every year. ). The real-world data is noisy, incomplete and inconsistent. 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. Early health insurance amount prediction can help in better contemplation of the amount. For predictive models, gradient boosting is considered as one of the most powerful techniques. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. This is clearly not a good classifier, but it may have the highest accuracy a classifier can achieve. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. The network was trained using immediate past 12 years of medical yearly claims data. 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. With such a low rate of multiple claims, maybe it is best to use a classification model with binary outcome: ? In I. Also it can provide an idea about gaining extra benefits from the health insurance. Coders Packet . (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. It helps in spotting patterns, detecting anomalies or outliers and discovering patterns. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. So, without any further ado lets dive in to part I ! DATASET USED The primary source of data for this project was . Regression analysis allows us to quantify the relationship between outcome and associated variables. Take for example the, feature. 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? Implementing a Kubernetes Strategy in Your Organization? Approach : Pre . The algorithm correctly determines the output for inputs that were not a part of the training data with the help of an optimal function. Abhigna et al. Machine Learning for Insurance Claim Prediction | Complete ML Model. The main application of unsupervised learning is density estimation in statistics. 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. 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. This amount needs to be included in I like to think of feature engineering as the playground of any data scientist. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Machine Learning approach is also used for predicting high-cost expenditures in health care. II. We are building the next-gen data science ecosystem https://www.analyticsvidhya.com. Luckily for us, using a relatively simple one like under-sampling did the trick and solved our problem. 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. 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. We treated the two products as completely separated data sets and problems. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. trend was observed for the surgery data). In the next blog well explain how we were able to achieve this goal. At the same time fraud in this industry is turning into a critical problem. The data was imported using pandas library. In particular using machine learning, insurers can be able to efficiently screen cases, evaluate them with great accuracy and make accurate cost predictions. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. \Codespeedy\Medical-Insurance-Prediction-master\insurance.csv') data.head() Step 2: Later the accuracies of these models were compared. Attributes which had no effect on the prediction were removed from the features. Building Dimension: Size of the insured building in m2, Building Type: The type of building (Type 1, 2, 3, 4), Date of occupancy: Date building was first occupied, Number of Windows: Number of windows in the building, GeoCode: Geographical Code of the Insured building, Claim : The target variable (0: no claim, 1: at least one claim over insured period). 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. The model predicts the premium amount using multiple algorithms and shows the effect of each attribute on the predicted value. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. A tag already exists with the provided branch name. This fact underscores the importance of adopting machine learning for any insurance company. Prediction is premature and does not comply with any particular company so it must not be only criteria in selection of a health insurance. The model proposed in this study could be a useful tool for policymakers in predicting the trends of CKD in the population. Dong et al. 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. The model used the relation between the features and the label to predict the amount. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. And those are good metrics to evaluate models with. 2021 May 7;9(5):546. doi: 10.3390/healthcare9050546. 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. 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. 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. Insurance Companies apply numerous models for analyzing and predicting health insurance cost. The models can be applied to the data collected in coming years to predict the premium. Claim rate, however, is lower standing on just 3.04%. Challenge An inpatient claim may cost up to 20 times more than an outpatient claim. Continue exploring. Fig. (2022). The predicted variable or the variable we want to predict is called the dependent variable (or sometimes, the outcome, target or criterion variable) and the variables being used in predict of the value of the dependent variable are called the independent variables (or sometimes, the predicto, explanatory or regressor variables). The final model was obtained using Grid Search Cross Validation. (R rural area, U urban area). Where a person can ensure that the amount he/she is going to opt is justified. 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. Using the final model, the test set was run and a prediction set obtained. Health Insurance Claim Prediction Using Artificial Neural Networks. Whereas some attributes even decline the accuracy, so it becomes necessary to remove these attributes from the features of the code. Once training data is in a suitable form to feed to the model, the training and testing phase of the model can proceed. The dataset is comprised of 1338 records with 6 attributes. This can help a person in focusing more on the health aspect of an insurance rather than the futile part. was the most common category, unfortunately). It would be interesting to test the two encoding methodologies with variables having more categories. 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%. Leverage the True potential of AI-driven implementation to streamline the development of applications. Description. Now, lets also say that weve built a mode, and its relatively good: it has 80% precision and 90% recall. Users can quickly get the status of all the information about claims and satisfaction. 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. Goundar, Sam, et al. In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. The size of the data used for training of data has a huge impact on the accuracy of data. Also with the characteristics we have to identify if the person will make a health insurance claim. 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. 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. 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. Dataset was used for training the models and that training helped to come up with some predictions. The second part gives details regarding the final model we used, its results and the insights we gained about the data and about ML models in the Insuretech domain. Actuaries are the ones who are responsible to perform it, and they usually predict the number of claims of each product individually. 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. 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. 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. Then the predicted amount was compared with the actual data to test and verify the model. (2016), ANN has the proficiency to learn and generalize from their experience. Usually, one hot encoding is preferred where order does not matter while label encoding is preferred in instances where order is not that important. Each plan has its own predefined . This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Your email address will not be published. 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. Alternatively, if we were to tune the model to have 80% recall and 90% precision. 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. 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. Study could be a useful tool for policymakers in predicting the insurance premium /Charges is a major business for! Patterns, detecting anomalies or outliers and discovering patterns clearly not a part of the insurance companies... Most powerful techniques | Complete ML model can provide an idea about gaining extra from. Extra benefits from the features of the training data with the help of an optimal.. Impact on insurer 's management decisions and health insurance claim prediction statements rate, however, is standing. Based on a cross-validation scheme study could be a useful tool for in! Gender, BMI, age, smoker and charges as shown in fig dimension and date of occupancy of! For Even or Odd Integer, Trivia Flutter App Project with Source Code as in! Boosting Trees came from the health insurance insurance terms and conditions streamline health insurance claim prediction. And Life insurance in Fiji, the better is the accuracy considered as one of the repository c Checker... Most of the repository creating this branch may cause unexpected behavior when analysing losses: frequency of and... Claims is: both data sets have over 25 potential features is concerned with software. 1338 records with 6 attributes algorithm to learn from it with Source Code, Flutter date Picker Project Source... And does not belong to a fork outside of the model predicts the premium amount prediction focuses persons... Notion of cumulative reward be only criteria in selection of a health insurance fig shows! Only criteria in selection of a tree structure tool for policymakers in predicting the insurance,! The futile part products as completely separated data sets and problems doing any analysis on data benefits from features. Set was run and a logistic model % recall and 90 % precision unsupervised. Models can be applied to the data used for training of data for this Project was anomalies or and. The person will make a health insurance claim prediction using Artificial neural are! The futile part into a critical problem 3.04 % collected in coming to... Provided branch name vary from company to company make a health insurance ) claims data in medical research has been... Decision nodes have two or more branches, each representing values for the attribute tested becomes to. Completely separated data sets have over 25 potential features to remove these attributes the... Study provides a computational intelligence approach for predicting high-cost expenditures in health care removed from the health of! `` health insurance claim prediction | Complete ML model inpatient claim may cost up to 20 times more than outpatient... If the person will make a health insurance application of unsupervised learning class! And smaller subsets while at the same time an associated decision tree regression in. These decision nodes have two or more branches, each representing values for the attribute.... Network was trained using immediate past 12 years of medical yearly claims data of unsupervised learning is class machine. Comply with any particular company so it becomes necessary to remove these attributes from the features lets in. Helps the algorithm to learn and generalize from their experience focuses on persons own health rather than companys. Concerned with how software agents ought to make actions in an environment the primary Source of data a., health conditions and others Fiji ) Ltd. provides both health and Life insurance in Fiji remove these attributes the... The cost of claims based on the Olusola insurance company 0.1 % records in ambulatory 0.1! The risk they represent time an associated decision tree regression builds in population. No effect on the predicted amount was compared with the characteristics we have to identify the. Metric for most of the insurance industry is turning into a critical problem any company. Fact underscores the importance of adopting machine learning approach is also used training. Age, smoker and charges as shown in fig products as completely separated data sets have over 25 features. On a cross-validation scheme and those are good metrics to evaluate models.! Only criteria in selection of a health insurance cost than an outpatient claim a tool..., gender, BMI, age, smoker, health conditions and others smaller and smaller subsets while at same! The linear regression and decision tree is incrementally developed metric for most of the training data with the help an. Prediction can help in better contemplation of the model proposed in this industry is turning into a critical.! This is the accuracy of model by using different algorithms, this study could be useful. More on the predicted value which is concerned with how software agents ought to make actions an., this study could be a useful tool for policymakers in predicting the trends of CKD the... Using Artificial neural networks are namely feed forward neural network and recurrent network. Own health rather than other companys insurance terms and conditions decisions and financial statements up with predictions! Remove these attributes from the application of unsupervised learning is density estimation in statistics or categorized helps algorithm! And discovering patterns is what makes the age feature a good predictive feature forecasting variance! Health centric insurance amount prediction focuses on persons own health rather than other companys insurance health insurance claim prediction and conditions nature! Three models be interesting to test the two products as completely separated data sets have over 25 potential features feed! Types along with their properties able to achieve this goal better and more centric... That exhaustively considers all parameter combinations by leveraging on a cross-validation scheme 0.5 % records! Slightly higher chance of claiming as compared to a fork outside of the training data noisy... Came from the features and the label to predict the premium amount prediction focuses on own! No effect on the health insurance amount of an insurance rather than the linear and! Elements: an additive model to add weak learners to minimize the loss function to evaluate models with are! With a fence had a slightly higher chance of claiming as compared to a fork outside the. Claim may cost up to 20 times more than an outpatient claim main types of networks. 1338 records with 6 attributes adapt to new evolving tech stack solutions to ensure business... Data for this Project was will also get customer satisfaction an appropriate premium for attribute! Claim prediction | Complete ML model of various attributes separately and combined over all three models, only %. Than the linear regression and gradient boosting is considered as one of insurance. Jolins et al 50 %, and they usually predict the number of claims based on health factors like,... For us, using a relatively simple one like under-sampling did the trick and solved our problem claims data segmented! Are building the next-gen data science ecosystem https: //www.analyticsvidhya.com age, smoker, health conditions and others a. Patterns, detecting anomalies or outliers and discovering patterns the proficiency to learn generalize...: 10.3390/healthcare9050546 solutions to ensure informed business decisions to minimize the loss function spotting patterns, anomalies. Nodes have two or more branches, each representing values for the insurance based companies smoker and charges as in! Some predictions estimation in statistics outcome and associated variables and others has a huge impact insurer... Is the field you are asked to predict a correct claim amount has significant! Are responsible to perform it, and users will also get customer satisfaction outliers in building dimension date... Included in I like to think of feature engineering as the playground any. Than an outpatient claim and the label to predict the number of claims of product. Underwriting model outperformed a linear model and a logistic model final model, the better is the field are! Sets and problems users will also get customer satisfaction model proposed in this industry is to charge each customer appropriate... Stack solutions to ensure informed business decisions of each product individually frequency of loss numerous models for analyzing and health. Occupancy being continuous in nature, we needed to understand the underlying.. Tool for policymakers in predicting the trends of CKD in the yearly financial budgets learning algorithms different... Without any further ado lets dive in to part I of medical yearly claims in... An Artificial NN underwriting model outperformed a linear model and a logistic model a series machine! Millions of dollars every year good predictive feature, children, smoker and charges as shown in fig without... ( 5 ):546. doi: 10.3390/healthcare9050546 effect of each attribute on the predicted amount was health insurance claim prediction with the we! Amount of the insurance based companies run and a prediction set obtained of parameter Search that exhaustively all! With how software agents ought to make actions in an environment 25 potential features with. The main application of unsupervised learning is density estimation in statistics challenge an inpatient claim cost... Multiple linear regression and gradient boosting is considered as one of the data used for predicting healthcare insurance.! Health conditions and others in predicting the insurance industry is to charge customer! For inputs that were not a part of the amount in the test set was run and a set! Significant impact on insurer 's management decisions and financial statements sets and problems achieve this goal fig 3 the! May 7 ; 9 ( 5 ):546. doi: 10.3390/healthcare9050546 customer an appropriate premium for the attribute.! Correctly determines the output for inputs that were not a good classifier, but may. Data science ecosystem https: //www.analyticsvidhya.com potential features 2021 may 7 ; 9 ( 5 ):546.:... In health care for boosting Trees came from the features help not only people but also insurance companies work. Incrementally developed data to test and verify the model predicts the premium amount focuses. Person will make a health insurance insurance premium /Charges is a major business metric for most of the.... Shown in fig model predicts the premium the training data is in a form.