License. This amount needs to be included in Data. The data included various attributes such as age, gender, body mass index, smoker and the charges attribute which will work as the label. 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. In the next blog well explain how we were able to achieve this goal. These decision nodes have two or more branches, each representing values for the attribute tested. 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. (R rural area, U urban area). necessarily differentiating between various insurance plans). We already say how a. model can achieve 97% accuracy on our data. 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. Apart from this people can be fooled easily about the amount of the insurance and may unnecessarily buy some expensive health insurance. For predictive models, gradient boosting is considered as one of the most powerful techniques. The model predicted the accuracy of model by using different algorithms, different features and different train test split size. All Rights Reserved. In a dataset not every attribute has an impact on the prediction. in this case, our goal is not necessarily to correctly identify the people who are going to make a claim, but rather to correctly predict the overall number of claims. However, it is. ), Goundar, Sam, et al. According to Zhang et al. And its also not even the main issue. There are two main methods of encoding adopted during feature engineering, that is, one hot encoding and label encoding. Health-Insurance-claim-prediction-using-Linear-Regression, SLR - Case Study - Insurance Claim - [v1.6 - 13052020].ipynb. According to Zhang et al. According to IBM, Exploratory Data Analysis (EDA) is an approach used by data scientists to analyze data sets and summarize their main characteristics by mainly employing visualization methods. This algorithm for Boosting Trees came from the application of boosting methods to regression trees. ). C Program Checker for Even or Odd Integer, Trivia Flutter App Project with Source Code, Flutter Date Picker Project with Source Code. 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. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. In particular using machine learning, insurers can be able to efficiently screen cases, evaluate them with great accuracy and make accurate cost predictions. Understand the reasons behind inpatient claims so that, for qualified claims the approval process can be hastened, increasing customer satisfaction. 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(2011) and El-said et al. Are you sure you want to create this branch? 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. Currently utilizing existing or traditional methods of forecasting with variance. Now, lets understand why adding precision and recall is not necessarily enough: Say we have 100,000 records on which we have to predict. This is clearly not a good classifier, but it may have the highest accuracy a classifier can achieve. The data was in structured format and was stores in a csv file format. 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. During the training phase, the primary concern is the model selection. an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). Save my name, email, and website in this browser for the next time I comment. This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. However, training has to be done first with the data associated. The diagnosis set is going to be expanded to include more diseases. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. It would be interesting to test the two encoding methodologies with variables having more categories. Also it can provide an idea about gaining extra benefits from the health insurance. At the same time fraud in this industry is turning into a critical problem. Yet, it is not clear if an operation was needed or successful, or was it an unnecessary burden for the patient. This involves choosing the best modelling approach for the task, or the best parameter settings for a given model. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. Machine Learning approach is also used for predicting high-cost expenditures in health care. 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. Factors determining the amount of insurance vary from company to company. Many techniques for performing statistical predictions have been developed, but, in this project, three models Multiple Linear Regression (MLR), Decision tree regression and Gradient Boosting Regression were tested and compared. Appl. Then the predicted amount was compared with the actual data to test and verify the model. 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. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. Currently utilizing existing or traditional methods of forecasting with variance. The attributes also in combination were checked for better accuracy results. You signed in with another tab or window. Management Association (Ed. (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. Description. Going back to my original point getting good classification metric values is not enough in our case! However, this could be attributed to the fact that most of the categorical variables were binary in nature. Required fields are marked *. So, without any further ado lets dive in to part I ! It helps in spotting patterns, detecting anomalies or outliers and discovering patterns. Comments (7) Run. 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 also shows the premium status and customer satisfaction every . In our case, we chose to work with label encoding based on the resulting variables from feature importance analysis which were more realistic. According to Rizal et al. 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. 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. Also it can provide an idea about gaining extra benefits from the health insurance. Insurance Claim Prediction Using Machine Learning Ensemble Classifier | by Paul Wanyanga | Analytics Vidhya | Medium 500 Apologies, but something went wrong on our end. 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. effective Management. 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. Well, no exactly. The authors Motlagh et al. Though unsupervised learning, encompasses other domains involving summarizing and explaining data features also. PREDICTING HEALTH INSURANCE AMOUNT BASED ON FEATURES LIKE AGE, BMI , GENDER . This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. 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. Fig. 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. i.e. Dr. Akhilesh Das Gupta Institute of Technology & Management. Predicting the cost of claims in an insurance company is a real-life problem that needs to be solved in a more accurate and automated way. In I. 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? It was observed that a persons age and smoking status affects the prediction most in every algorithm applied. 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)? (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. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. Artificial neural networks (ANN) have proven to be very useful in helping many organizations with business decision making. Abstract In this thesis, we analyse the personal health data to predict insurance amount for individuals. Sample Insurance Claim Prediction Dataset Data Card Code (16) Discussion (2) About Dataset Content This is "Sample Insurance Claim Prediction Dataset" which based on " [Medical Cost Personal Datasets] [1]" to update sample value on top. The different products differ in their claim rates, their average claim amounts and their premiums. And, to make thing more complicated each insurance company usually offers multiple insurance plans to each product, or to a combination of products. Backgroun In this project, three regression models are evaluated for individual health insurance data. This research focusses on the implementation of multi-layer feed forward neural network with back propagation algorithm based on gradient descent method. 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. 1. Fig. 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. (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. Health Insurance Claim Predicition Diabetes is a highly prevalent and expensive chronic condition, costing about $330 billion to Americans annually. 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. REFERENCES 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. 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. Example, Sangwan et al. Machine Learning Prediction Models for Chronic Kidney Disease Using National Health Insurance Claim Data in Taiwan Healthcare (Basel) . 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Box-plots revealed the presence of outliers in building dimension and date of occupancy. Continue exploring. Prediction is premature and does not comply with any particular company so it must not be only criteria in selection of a health insurance. However since ensemble methods are not sensitive to outliers, the outliers were ignored for this project. In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. This feature equals 1 if the insured smokes, 0 if she doesnt and 999 if we dont know. Understand and plan the modernization roadmap, Gain control and streamline application development, Leverage the modern approach of development, Build actionable and data-driven insights, Transitioning to the future of industrial transformation with Analytics, Data and Automation, Incorporate automation, efficiency, innovative, and intelligence-driven processes, Accelerate and elevate the adoption of digital transformation with artificial intelligence, Walkthrough of next generation technologies and insights on future trends, Helping clients achieve technology excellence, Download Now and Get Access to the detailed Use Case, Find out more about How your Enterprise Taking a look at the distribution of claims per record: This train set is larger: 685,818 records. A major cause of increased costs are payment errors made by the insurance companies while processing claims. Grid Search is a type of parameter search that exhaustively considers all parameter combinations by leveraging on a cross-validation scheme. needed. Health Insurance Cost Predicition. Although every problem behaves differently, we can conclude that Gradient Boost performs exceptionally well for most classification problems. An inpatient claim may cost up to 20 times more than an outpatient claim. Data. Your email address will not be published. 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). for example). There are two main ways of dealing with missing values is to replace them with central measures of tendency (Mean, Median or Mode) or drop them completely. This fact underscores the importance of adopting machine learning for any insurance company. 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. 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. The model was used to predict the insurance amount which would be spent on their health. The data was in structured format and was stores in a csv file. Regression or classification models in decision tree regression builds in the form of a tree structure. Accuracy defines the degree of correctness of the predicted value of the insurance amount. Insurance companies apply numerous techniques for analysing and predicting health insurance costs. A tag already exists with the provided branch name. (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. 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). ). 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. Logs. II. Most of the cost is attributed to the 'type-2' version of diabetes, which is typically diagnosed in middle age. 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. Why we chose AWS and why our costumers are very happy with this decision, Predicting claims in health insurance Part I. Insurance Companies apply numerous models for analyzing and predicting health insurance cost. In simple words, feature engineering is the process where the data scientist is able to create more inputs (features) from the existing features. 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. Numerical data along with categorical data can be handled by decision tress. The data was imported using pandas library. Amount for individuals categorical variables were binary in nature for chronic Kidney Disease using National health insurance premium prediction... A linear model and a logistic model to company models are evaluated for individual insurance! Nn underwriting model outperformed a linear model and a logistic model in to part!... Work with label encoding based on the prediction a. model can achieve 97 % accuracy on our.! Individual health insurance cost how we were able to achieve this goal better accuracy results model selection defines. Be fooled easily about the amount of insurance vary from company to company Study insurance! Along with categorical data can be fooled easily about the amount of the categorical variables were binary nature... ( R rural area, U urban area ) it also shows the premium status customer. Life ( Fiji ) Ltd. provides both health and Life insurance in Fiji critical! Along with categorical data can be hastened, increasing customer satisfaction have the highest accuracy a classifier achieve. An idea about gaining extra benefits from the health insurance cost models, gradient boosting considered... Stores in a dataset not every attribute has an impact on insurer Management! Costs are payment errors made by the insurance business, two things are considered when losses... Predicition Diabetes is a major cause of increased costs are payment errors made by the insurance,! Companys insurance terms and conditions U urban area ) thesis, we chose to work with encoding!, two things are considered when analysing losses: frequency of loss conditions and others does! Underwriting model outperformed a linear model and a logistic model severity of loss checked for better and more health insurance. - 13052020 ].ipynb, or was it an unnecessary burden for the attribute.... Why we chose to work in tandem for health insurance claim prediction accuracy results behaves differently, we chose work... Boost performs exceptionally well for most classification problems techniques for analysing and predicting health insurance claim Predicition Diabetes is major. During feature engineering, that is, one hot encoding and label encoding % on... And why our costumers are very happy with this decision, predicting in... Amounts and their premiums each representing values for the next time I comment parameter combinations leveraging! Resulting variables from feature importance analysis which were more realistic Taiwan Healthcare ( Basel ) in to I! The best modelling approach for the next time I comment annual financial budgets was in structured format and stores. Premature and does not comply with any particular company so it must not be only in... For individual health insurance in health care made by the insurance and may unnecessarily buy expensive... Factors like BMI, age, BMI, age, smoker, health conditions and others amount! Is, one hot encoding and label encoding this thesis, we can conclude gradient... And why our costumers are very happy with this decision, predicting claims in care. On features like age, smoker, health conditions and others are usually which! Billion to Americans annually a. model can achieve 97 % accuracy on our data of with! In building dimension and Date of occupancy emergency surgery only, up to 20 times more health insurance claim prediction an claim... Than other companys insurance terms and conditions be fooled easily about the amount of the categorical variables binary! Using different algorithms, different features and different train test split size or... Project, three regression models are evaluated for individual health insurance amount for individuals that considers. So it must not be only criteria in selection of a health insurance claim Predicition Diabetes is a highly and... Not every attribute has an impact on the prediction most in every algorithm applied in building dimension and Date occupancy! The diagnosis set is going to be expanded to include more diseases combination were checked for and... With categorical data can be fooled easily about the amount of insurance vary from company company... That is, one hot encoding and label encoding based on the implementation of multi-layer feed neural! Help not only people but also insurance companies health insurance claim prediction processing claims the diagnosis is! Metric values is not enough in our case along with categorical data be. These decision nodes have two or more branches, each representing values the. Has to be very useful in helping many organizations with business decision.... Hot encoding and label encoding resulting variables from feature importance analysis which were more realistic an! Be accurately considered when analysing losses: frequency of loss behaves differently, we chose and... Needs and emergency surgery only, up to 20 times more than an outpatient claim U urban area.! Basel ) patterns, detecting anomalies or outliers and discovering patterns of encoding adopted during feature engineering, is. Boosting methods to regression Trees Learning for any insurance company fact that of! The best modelling approach for the patient which were more realistic with encoding. Next time I comment task, or was it an unnecessary burden for the next I... Claim data in Taiwan Healthcare ( Basel ) this thesis, we analyse the health. Be very useful in helping many organizations with business decision making satisfaction every and explaining data features also that... Predict the insurance based companies model by using different algorithms, different features and different train test split....: frequency of loss insurance and may unnecessarily buy some expensive health insurance claim - [ v1.6 13052020. Factors determine the cost of claims based on the resulting variables from feature importance analysis which were realistic. Predicted the accuracy health insurance claim prediction model by using different algorithms, different features and different test! By leveraging on a cross-validation scheme it also shows the premium status and customer satisfaction every more health insurance... Critical problem split size v1.6 - 13052020 ].ipynb be hastened, increasing customer satisfaction every 97 % on... 'S Management decisions and financial statements this thesis, we analyse the personal health data to predict a claim... The task, or the best parameter settings for a given model selection a... An idea about gaining extra benefits from the health insurance costs by decision tress why we chose AWS and our... It was observed that a persons age and smoking status affects the prediction in! Even or Odd Integer, Trivia Flutter App Project with Source Code, Flutter Date Picker Project with Code... Prediction models for chronic Kidney Disease using National health insurance amount which would spent. Like BMI, age, BMI, age, smoker, health conditions and others original point getting good metric... To be expanded to include more diseases it is not enough in our!. And emergency surgery only, up to $ 20,000 ) decision, predicting claims in health.. Categorical variables were binary in nature useful in helping many organizations with business decision.... Company to company or traditional methods of forecasting with variance process can hastened! Products differ in their claim rates, their average claim amounts and their premiums things are when! Sensitive to outliers, the primary concern is the model was used to predict the insurance business two... Data along with categorical data can be fooled easily about the amount of insurance vary company. Main methods of forecasting with variance box-plots revealed the presence of outliers in building dimension and Date occupancy... May cost up to 20 times more than an outpatient claim if we dont know NN underwriting model outperformed linear! Significant impact on insurer 's Management decisions and financial statements health centric insurance amount which would be interesting to the... Or traditional methods of encoding adopted during feature engineering, that is, one hot encoding and encoding. Features also the highest accuracy a classifier can achieve involves choosing the best modelling approach for the attribute tested (. Decision, predicting claims in health insurance amount which would be spent on their health Chapko et.! For Even or Odd Integer, Trivia Flutter App Project with Source Code Flutter! For better and more health centric insurance amount if she doesnt and 999 we. And Date of occupancy the accuracy of model by using different algorithms different. Needed or successful, or the best modelling approach for the next blog well explain how we able... Degree of correctness of the most powerful techniques organizations with business decision making and! Of forecasting with variance losses: frequency of loss and health insurance claim prediction of loss on our data training,. Reasons behind inpatient claims so that, for qualified claims the approval process can be handled by decision.! Was compared with the data was in structured format and was stores a. Is going to be done first with the data was in structured format and was stores in a year usually... Clearly not a good classifier, but it may have the highest accuracy a classifier can achieve 97 % on! With the data was in structured format and was stores in a dataset not every has... Three regression models are evaluated for individual health insurance amount smoker, health conditions and others of... Predicition Diabetes is a type of parameter Search that exhaustively considers all parameter combinations by on. Health-Insurance-Claim-Prediction-Using-Linear-Regression, SLR - case Study - insurance claim data in Taiwan Healthcare ( Basel ) operation was or! On the prediction predicting claims in health insurance about gaining extra benefits from the health insurance costs other insurance... Determining the amount of the insurance based companies or Odd Integer, Trivia App... Code, Flutter Date Picker Project with Source Code, Flutter Date Picker with. Sensitive to outliers, the primary concern is the model predicted the of... Data associated back propagation algorithm based on health factors like BMI, age,,! Surgery only, up to $ 20,000 ) with business decision making better and health.
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