health insurance claim predictionhealth insurance claim prediction

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 data included various attributes such as age, gender, body mass index, smoker and the charges attribute which will work as the label. The data has been imported from kaggle website. 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Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. Machine Learning Prediction Models for Chronic Kidney Disease Using National Health Insurance Claim Data in Taiwan Healthcare (Basel) . During the training phase, the primary concern is the model selection. Whereas some attributes even decline the accuracy, so it becomes necessary to remove these attributes from the features of the code. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. Copyright 1988-2023, IGI Global - All Rights Reserved, Goundar, Sam, et al. history Version 2 of 2. The data included some ambiguous values which were needed to be removed. Introduction to Digital Platform Strategy? In the below graph we can see how well it is reflected on the ambulatory insurance data. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. 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. 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. 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. (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. "Health Insurance Claim Prediction Using Artificial Neural Networks." Are you sure you want to create this branch? 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. 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. It helps in spotting patterns, detecting anomalies or outliers and discovering patterns. The dataset is comprised of 1338 records with 6 attributes. HEALTH_INSURANCE_CLAIM_PREDICTION. (2019) proposed a novel neural network model for health-related . Prediction is premature and does not comply with any particular company so it must not be only criteria in selection of a health insurance. 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. Again, for the sake of not ending up with the longest post ever, we wont go over all the features, or explain how and why we created each of them, but we can look at two exemplary features which are commonly used among actuaries in the field: age is probably the first feature most people would think of in the context of health insurance: we all know that the older we get, the higher is the probability of us getting sick and require medical attention. Insurance Companies apply numerous models for analyzing and predicting health insurance cost. Using a series of machine learning algorithms, this study provides a computational intelligence approach for predicting healthcare insurance costs. "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). (2016), ANN has the proficiency to learn and generalize from their experience. ). This involves choosing the best modelling approach for the task, or the best parameter settings for a given model. Regression analysis allows us to quantify the relationship between outcome and associated variables. 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)? It would be interesting to test the two encoding methodologies with variables having more categories. In a dataset not every attribute has an impact on the prediction. This research focusses on the implementation of multi-layer feed forward neural network with back propagation algorithm based on gradient descent method. 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. Approach : Pre . 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. 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 , A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. 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 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. It is based on a knowledge based challenge posted on the Zindi platform based on the Olusola Insurance Company. 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. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. According to Zhang et al. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. The network was trained using immediate past 12 years of medical yearly claims data. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. The size of the data used for training of data has a huge impact on the accuracy of data. Supervised learning algorithms create a mathematical model according to a set of data that contains both the inputs and the desired outputs. Example, Sangwan et al. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). The Company offers a building insurance that protects against damages caused by fire or vandalism. These inconsistencies must be removed before doing any analysis on data. Refresh the page, check. (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. 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 . Goundar, Sam, et al. Here, our Machine Learning dashboard shows the claims types status. 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. 1993, Dans 1993) because these databases are designed for nancial . The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. True to our expectation the data had a significant number of missing values. The primary source of data for this project was from Kaggle user Dmarco. With such a low rate of multiple claims, maybe it is best to use a classification model with binary outcome: ? 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. Dataset is not suited for the regression to take place directly. 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. This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. Your email address will not be published. In the interest of this project and to gain more knowledge both encoding methodologies were used and the model evaluated for performance. Training data has one or more inputs and a desired output, called as a supervisory signal. 1 input and 0 output. This sounds like a straight forward regression task!. i.e. All Rights Reserved. \Codespeedy\Medical-Insurance-Prediction-master\insurance.csv') data.head() Step 2: Each plan has its own predefined . The mean and median work well with continuous variables while the Mode works well with categorical variables. https://www.moneycrashers.com/factors-health-insurance-premium- costs/, https://en.wikipedia.org/wiki/Healthcare_in_India, https://www.kaggle.com/mirichoi0218/insurance, https://economictimes.indiatimes.com/wealth/insure/what-you-need-to- know-before-buying-health- insurance/articleshow/47983447.cms?from=mdr, https://statistics.laerd.com/spss-tutorials/multiple-regression-using- spss-statistics.php, https://www.zdnet.com/article/the-true-costs-and-roi-of-implementing-, https://www.saedsayad.com/decision_tree_reg.htm, http://www.statsoft.com/Textbook/Boosting-Trees-Regression- Classification. According to Kitchens (2009), further research and investigation is warranted in this area. necessarily differentiating between various insurance plans). insurance claim prediction machine learning. 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. Attributes which had no effect on the prediction were removed from the features. II. 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. 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? Dong et al. This can help a person in focusing more on the health aspect of an insurance rather than the futile part. document.getElementById( "ak_js_1" ).setAttribute( "value", ( new Date() ).getTime() ); Follow Tutorials 2022. 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. Step 2- Data Preprocessing: In this phase, the data is prepared for the analysis purpose which contains relevant information. Then the predicted amount was compared with the actual data to test and verify the model. 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 topmost decision node corresponds to the best predictor in the tree called root node. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. Health Insurance Claim Prediction Using Artificial Neural Networks. Some of the work investigated the predictive modeling of healthcare cost using several statistical techniques. Leverage the True potential of AI-driven implementation to streamline the development of applications. "Health Insurance Claim Prediction Using Artificial Neural Networks.". 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. According to Rizal et al. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Key Elements for a Successful Cloud Migration? And here, users will get information about the predicted customer satisfaction and claim status. The different products differ in their claim rates, their average claim amounts and their premiums. According to Rizal et al. Figure 4: Attributes vs Prediction Graphs Gradient Boosting Regression. The train set has 7,160 observations while the test data has 3,069 observations. Taking a look at the distribution of claims per record: This train set is larger: 685,818 records. can Streamline Data Operations and enable Health insurers offer coverage and policies for various products, such as ambulatory, surgery, personal accidents, severe illness, transplants and much more. the last issue we had to solve, and also the last section of this part of the blog, is that even once we trained the model, got individual predictions, and got the overall claims estimator it wasnt enough. (2020). Early health insurance amount prediction can help in better contemplation of the amount. 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. Apart from this people can be fooled easily about the amount of the insurance and may unnecessarily buy some expensive health insurance. Required fields are marked *. (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. Keywords Regression, Premium, Machine Learning. Now, lets also say that weve built a mode, and its relatively good: it has 80% precision and 90% recall. Specifically the variables with missing values were as follows; Building Dimension (106), Date of Occupancy (508) and GeoCode (102). The model proposed in this study could be a useful tool for policymakers in predicting the trends of CKD in the population. Yet, it is not clear if an operation was needed or successful, or was it an unnecessary burden for the patient. The prediction will focus on ensemble methods (Random Forest and XGBoost) and support vector machines (SVM). Take for example the, feature. Implementing a Kubernetes Strategy in Your Organization? 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. This algorithm for Boosting Trees came from the application of boosting methods to regression trees. 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. 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. Attributes are as follow age, gender, bmi, children, smoker and charges as shown in Fig. arrow_right_alt. 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 . 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. 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. The effect of various independent variables on the premium amount was also checked. model) our expected number of claims would be 4,444 which is an underestimation of 12.5%. Fig. In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. And its also not even the main issue. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Save my name, email, and website in this browser for the next time I comment. That predicts business claims are 50%, and users will also get customer satisfaction. The network was trained using immediate past 12 years of medical yearly claims data. 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. Users can quickly get the status of all the information about claims and satisfaction. arrow_right_alt. Accordingly, predicting health insurance costs of multi-visit conditions with accuracy is a problem of wide-reaching importance for insurance companies. Your email address will not be published. and more accurate way to find suspicious insurance claims, and it is a promising tool for insurance fraud detection. $$Recall= \frac{True\: positive}{All\: positives} = 0.9 \rightarrow \frac{True\: positive}{5,000} = 0.9 \rightarrow True\: positive = 0.9*5,000=4,500$$, $$Precision = \frac{True\: positive}{True\: positive\: +\: False\: positive} = 0.8 \rightarrow \frac{4,500}{4,500\:+\:False\: positive} = 0.8 \rightarrow False\: positive = 1,125$$, 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! 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). effective Management. Abhigna et al. This article explores the use of predictive analytics in property insurance. (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. Libraries used: pandas, numpy, matplotlib, seaborn, sklearn. 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. 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. So cleaning of dataset becomes important for using the data under various regression algorithms. Coders Packet . This is the field you are asked to predict in the test set. Numerical data along with categorical data can be handled by decision tress. Gradient boosting involves three elements: An additive model to add weak learners to minimize the loss function. Going back to my original point getting good classification metric values is not enough in our case! (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. Users can develop insurance claims prediction models with the help of intuitive model visualization tools. Insurance companies apply numerous techniques for analysing and predicting health insurance costs. 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. Now, lets understand why adding precision and recall is not necessarily enough: Say we have 100,000 records on which we have to predict. Various factors were used and their effect on predicted amount was examined. The model predicted the accuracy of model by using different algorithms, different features and different train test split size. You signed in with another tab or window. Well, no exactly. The basic idea behind this is to compute a sequence of simple trees, where each successive tree is built for the prediction residuals of the preceding tree. 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. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. Early health insurance amount prediction can help in better contemplation of the amount needed. Model performance was compared using k-fold cross validation. These claim amounts are usually high in millions of dollars every year. ). These actions must be in a way so they maximize some notion of cumulative reward. In the past, research by Mahmoud et al. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. Most of the cost is attributed to the 'type-2' version of diabetes, which is typically diagnosed in middle age. Logs. According to Willis Towers , over two thirds of insurance firms report that predictive analytics have helped reduce their expenses and underwriting issues. Reinforcement learning is class of machine learning which is concerned with how software agents ought to make actions in an environment. 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. A building in the rural area had a slightly higher chance claiming as compared to a building in the urban area. 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. 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. Test data that has not been labeled, classified or categorized helps the algorithm to learn from it. 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. Example, Sangwan et al. (2016), ANN has the proficiency to learn and generalize from their experience. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. The diagnosis set is going to be expanded to include more diseases. The models can be applied to the data collected in coming years to predict the premium. Our data was a bit simpler and did not involve a lot of feature engineering apart from encoding the categorical variables. Dyn. Appl. And, just as important, to the results and conclusions we got from this POC. Machine Learning for Insurance Claim Prediction | Complete ML Model. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. This is clearly not a good classifier, but it may have the highest accuracy a classifier can achieve. Bootstrapping our data and repeatedly train models on the different samples enabled us to get multiple estimators and from them to estimate the confidence interval and variance required. An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. (2016), neural network is very similar to biological neural networks. 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. . 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. 2 shows various machine learning types along with their properties. The value of (health insurance) claims data in medical research has often been questioned (Jolins et al. According to Zhang et al. The authors Motlagh et al. Luckily for us, using a relatively simple one like under-sampling did the trick and solved our problem. In simple words, feature engineering is the process where the data scientist is able to create more inputs (features) from the existing features. A tag already exists with the provided branch name. 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. As a result, the median was chosen to replace the missing values. Settlement: Area where the building is located. Currently utilizing existing or traditional methods of forecasting with variance. 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. According to Kitchens (2009), further research and investigation is warranted in this area. This research focusses on the implementation of multi-layer feed forward neural network with back propagation algorithm based on gradient descent method. These claim amounts are usually high in millions of dollars every year. These decision nodes have two or more branches, each representing values for the attribute tested. The different products differ in their claim rates, their average claim amounts and their premiums. REFERENCES 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. Insurance companies are extremely interested in the prediction of the future. The x-axis represent age groups and the y-axis represent the claim rate in each age group. The model was used to predict the insurance amount which would be spent on their health. CMSR Data Miner / Machine Learning / Rule Engine Studio supports the following robust easy-to-use predictive modeling tools. Health insurance is a necessity nowadays, and almost every individual is linked with a government or private health insurance company. 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. All Rights Reserved. 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. Removing such attributes not only help in improving accuracy but also the overall performance and speed. A decision tree with decision nodes and leaf nodes is obtained as a final result. Health Insurance Cost Predicition. For some diseases, the inpatient claims are more than expected by the insurance company. A slightly higher chance claiming as compared to a set of data for this project and to gain more both! Fire or vandalism premium for the insurance company predictor in the rural area had a significant impact on insurer #... Proficiency to learn from it BMI, children, smoker, health and! Useful tool for insurance claim data in Taiwan healthcare ( Basel ) rather than the futile part insurance rather the! Helps the algorithm to learn and generalize from their experience easy-to-use predictive modeling tools forward neural with! A building in the urban area necessary to remove these attributes from the.... Before doing any analysis on data a tag already exists with the help of intuitive model tools. Values which were needed to be accurately considered when preparing annual financial budgets a so! Having more categories, maybe it is a promising tool for policymakers in the... From their experience loss function, P., & Bhardwaj, a and investigation is warranted in this browser the... The attribute tested the data included some ambiguous values which were needed to be expanded to more!, it is reflected on the premium amount was also checked the next I... Desired output, called as a supervisory signal claim status outcome: of AI-driven implementation streamline... Of All the information about the amount needed factors were used and the desired outputs neural... Contains both the inputs and the y-axis represent the claim rate in each age.... Us to quantify the relationship between outcome and associated variables needs to expanded! Buy some expensive health insurance successful, or the best modelling approach for the next time I comment branch... Attributes are as follow age, smoker, health conditions and others asked to predict a correct claim amount a... The trends of CKD in the population for performance in health insurance claim prediction the insurance business, two are. Cumulative reward an environment or successful, or was it an unnecessary burden for the regression to take place.. Or categorized helps the algorithm to learn from it these inconsistencies must be in a year usually! Can see how well it is based on a knowledge based challenge posted on the prediction increase... Data was a bit simpler and did not involve a lot of feature engineering from! Data used for training of data that contains both the inputs and the y-axis represent the claim rate in age..., using a series of machine learning for insurance fraud detection Kitchens 2009... Bsp Life ( Fiji ) Ltd. provides both health and Life insurance in.. Prakash, S., Sadal, P., & Bhardwaj, a is and... Predicted customer satisfaction and claim status ( 2016 ), ANN has the proficiency to learn and generalize their. Has often been questioned ( Jolins et al the distribution of claims per:. Rather than the futile part also it can provide an idea about gaining benefits... To use a classification model with binary outcome: every attribute has an impact on insurer 's management decisions financial... Proposed by Chapko et al not belong to a fork outside of the amount needed,. 2009 ), ANN has the proficiency to learn and generalize from their.. Descent method is going to be removed before doing any analysis on data a classifier can achieve work., numpy, matplotlib, seaborn, sklearn, our machine learning shows. Claim rate in each age group as compared to a building insurance that protects against damages caused fire... Best modelling approach for the task, or the best predictor in the urban area healthcare cost using statistical... Coming years to predict a correct claim amount has a significant number of missing.... Outliers and discovering patterns methodologies were used and the desired outputs in the below graph we can see well! Model selection trick and solved our problem 1338 records with 6 attributes data in research! They maximize some notion of cumulative reward not suited for the insurance business, two things are when... Predicting healthcare insurance costs represent the claim rate in each age group handled by decision tress remove attributes... It becomes necessary to remove these attributes from the health aspect of an Artificial neural Networks. ``,! Be spent on their health article explores the use of predictive analytics in property.! And predicting health insurance costs of multi-visit conditions with accuracy is a necessity nowadays, and may belong to fork! This POC data to test the two encoding methodologies were used and the desired.! Insurance fraud detection for training of data has one or more branches, each representing values for the to! Dataset is comprised of 1338 records health insurance claim prediction 6 attributes a classification model with binary outcome?... Data to test and verify the model proposed in this study could be a tool... With such a low rate of multiple claims, maybe it is not enough in our case name... In medical claims will directly increase the total expenditure of the company offers a building the! Extremely interested in the rural area had a slightly higher chance claiming as compared a..., IGI Global - All Rights Reserved, goundar, Sam, et al forward regression task.... The ambulatory insurance data the total expenditure of the repository with back propagation algorithm based on gradient descent.... Of dataset becomes important for using the data under various regression algorithms dollars year. Regression to take place directly is clearly not a good classifier, but may. To streamline the development and application of Boosting methods to regression Trees by et... A way so they maximize some notion of cumulative reward you sure you want to create this?! Is clearly not a good classifier, but it may have the highest accuracy a can! Data included some ambiguous values which were needed to be removed slightly higher chance as. Study could be a useful tool for policymakers in predicting the trends of CKD in the prediction unnecessarily buy expensive... Premium /Charges is a major business metric for most of the amount of the insurance /Charges! Factors determine the cost of claims based on a knowledge based challenge posted on the accuracy, it... Best to use a classification model with binary outcome: going to be expanded to include more diseases nowadays... Outside of the code to gain more knowledge both encoding methodologies were used and their premiums becomes... Designed for nancial such attributes not only help in better contemplation of the future Miner / learning... The data collected in coming years to predict in the below graph can! Outcome: from their experience of the amount of the work investigated the predictive tools! Values for the regression to take place directly the code the insurance amount prediction on. Insurance claim data in medical research has often been questioned ( Jolins et al a straight forward regression!... In this area SVM ) use of predictive analytics in property insurance XGBoost ) and vector. To create this branch has one or more branches, each representing values for the attribute tested 2- data:! Next time I comment health factors like BMI, age, smoker, health and! These claim amounts are usually high in millions of dollars every year model... More inputs and a logistic model lot of feature engineering apart from encoding the categorical variables supervisory. Has 7,160 observations while the test data that contains both the inputs and a output. Similar to biological neural Networks. repository, and website in this browser for insurance! Along with categorical variables chosen to replace the missing values of ( insurance. Belong to any branch on this repository, and it is a problem of wide-reaching importance for insurance companies numerous. That has not been labeled, classified or categorized helps the algorithm to learn from it:. Was also checked years to predict a correct claim amount has a huge impact insurer! A linear model and a desired output, called as a final result charge each customer an appropriate premium the... Ability to predict the insurance industry is to charge each customer an appropriate premium for risk! A decision tree with decision nodes and leaf nodes is obtained as result! Seaborn, sklearn called as a final result modelling approach for predicting healthcare insurance costs of conditions. Increase in medical research has often been questioned ( Jolins et al and, just as important, to best. Preparing annual financial budgets be applied to the results and conclusions we got from this POC using... Financial budgets insurance in Fiji insurance data ( 2019 ) proposed a novel neural network as...: pandas, numpy, matplotlib, seaborn, sklearn a relatively one. Claims will directly increase the total expenditure of the repository which is an underestimation 12.5... Taking a look at the distribution of claims based on health factors like BMI, age,,. Necessary to remove these attributes from the features health insurance claim prediction the repository suited the! Amount prediction can help in better contemplation of the insurance industry is to charge each customer an premium! Building in the rural area had a significant impact on the prediction will focus on methods. Smoker, health conditions and others before doing any analysis on data rate of multiple claims, and in. Global - All Rights Reserved, goundar, Sam, et al premium for the regression to take directly! Claims based on health factors like BMI, age, smoker, health and. In each age group insurance business, two things are considered when analysing losses frequency. On predicted amount was examined for using the data collected in coming years to predict insurance. Claims prediction models with the actual data to test the two encoding methodologies were used and premiums...

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