In fact, the term model selection often refers to both of these processes, as, in many cases, various models were tried first and best performing model (with the best performing parameter settings for each model) was selected. 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. Some of the work investigated the predictive modeling of healthcare cost using several statistical techniques. 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. (2011) and El-said et al. (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. (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. (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. In this article, we have been able to illustrate the use of different machine learning algorithms and in particular ensemble methods in claim prediction. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. Also it can provide an idea about gaining extra benefits from the health insurance. PREDICTING HEALTH INSURANCE AMOUNT BASED ON FEATURES LIKE AGE, BMI , GENDER . We treated the two products as completely separated data sets and problems. Also it can provide an idea about gaining extra benefits from the health insurance. For some diseases, the inpatient claims are more than expected by the insurance company. Removing such attributes not only help in improving accuracy but also the overall performance and speed. Dyn. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. The authors Motlagh et al. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. 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: 10.4018/IJSDA.2020070103: A number of numerical practices exist that actuaries use to predict annual medical claim expense in an insurance company. . Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. The distribution of number of claims is: Both data sets have over 25 potential features. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. In our case, we chose to work with label encoding based on the resulting variables from feature importance analysis which were more realistic. Supervised learning algorithms learn from a model containing function that can be used to predict the output from the new inputs through iterative optimization of an objective function. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. 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). model) our expected number of claims would be 4,444 which is an underestimation of 12.5%. Machine Learning approach is also used for predicting high-cost expenditures in health care. Fig 3 shows the accuracy percentage of various attributes separately and combined over all three models. Notebook. Key Elements for a Successful Cloud Migration? The primary source of data for this project was from Kaggle user Dmarco. According to Zhang et al. 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. Then the predicted amount was compared with the actual data to test and verify the model. The dataset is divided or segmented into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed. Also people in rural areas are unaware of the fact that the government of India provide free health insurance to those below poverty line. The model proposed in this study could be a useful tool for policymakers in predicting the trends of CKD in the population. And those are good metrics to evaluate models with. A building without a garden had a slightly higher chance of claiming as compared to a building with a garden. Attributes are as follow age, gender, bmi, children, smoker and charges as shown in Fig. Example, Sangwan et al. 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! Users can quickly get the status of all the information about claims and satisfaction. Usually, one hot encoding is preferred where order does not matter while label encoding is preferred in instances where order is not that important. Insurance companies apply numerous techniques for analysing and predicting health insurance costs. Specifically the variables with missing values were as follows; Building Dimension (106), Date of Occupancy (508) and GeoCode (102). Abhigna et al. Here, our Machine Learning dashboard shows the claims types status. 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? Last modified January 29, 2019, Your email address will not be published. Now, lets understand why adding precision and recall is not necessarily enough: Say we have 100,000 records on which we have to predict. 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. 11.5 second run - successful. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. Attributes which had no effect on the prediction were removed from the features. Using a series of machine learning algorithms, this study provides a computational intelligence approach for predicting healthcare insurance costs. Regression or classification models in decision tree regression builds in the form of a tree structure. Nidhi Bhardwaj , Rishabh Anand, 2020, Health Insurance Amount Prediction, INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT) Volume 09, Issue 05 (May 2020), Creative Commons Attribution 4.0 International License, Assessment of Groundwater Quality for Drinking and Irrigation use in Kumadvati watershed, Karnataka, India, Ergonomic Design and Development of Stair Climbing Wheel Chair, Fatigue Life Prediction of Cold Forged Punch for Fastener Manufacturing by FEA, Structural Feature of A Multi-Storey Building of Load Bearings Walls, Gate-All-Around FET based 6T SRAM Design Using a Device-Circuit Co-Optimization Framework, How To Improve Performance of High Traffic Web Applications, Cost and Waste Evaluation of Expanded Polystyrene (EPS) Model House in Kenya, Real Time Detection of Phishing Attacks in Edge Devices, Structural Design of Interlocking Concrete Paving Block, The Role and Potential of Information Technology in Agricultural Development. (2011) and El-said et al. Take for example the, feature. The train set has 7,160 observations while the test data has 3,069 observations. It would be interesting to test the two encoding methodologies with variables having more categories. 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. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. 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%. ), Goundar, Sam, et al. 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. Continue exploring. Neural networks can be distinguished into distinct types based on the architecture. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. Although every problem behaves differently, we can conclude that Gradient Boost performs exceptionally well for most classification problems. The insurance user's historical data can get data from accessible sources like. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 1 input and 0 output. These inconsistencies must be removed before doing any analysis on data. The data was in structured format and was stores in a csv file format. of a health insurance. and more accurate way to find suspicious insurance claims, and it is a promising tool for insurance fraud detection. Health Insurance Claim Prediction Using Artificial Neural Networks Authors: Akashdeep Bhardwaj University of Petroleum & Energy Studies Abstract and Figures A number of numerical practices exist. The first step was to check if our data had any missing values as this might impact highly on all other parts of the analysis. By filtering and various machine learning models accuracy can be improved. According to Rizal et al. The final model was obtained using Grid Search Cross Validation. 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. DATASET USED The primary source of data for this project was . Figure 4: Attributes vs Prediction Graphs Gradient Boosting Regression. In the next part of this blog well finally get to the modeling process! 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. 1. In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. effective Management. However, training has to be done first with the data associated. It was observed that a persons age and smoking status affects the prediction most in every algorithm applied. The network was trained using immediate past 12 years of medical yearly claims data. Each plan has its own predefined . Application and deployment of insurance risk models . Other two regression models also gave good accuracies about 80% In their prediction. was the most common category, unfortunately). As a result, the median was chosen to replace the missing values. 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. for example). Once training data is in a suitable form to feed to the model, the training and testing phase of the model can proceed. The algorithm correctly determines the output for inputs that were not a part of the training data with the help of an optimal function. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. The different products differ in their claim rates, their average claim amounts and their premiums. These claim amounts are usually high in millions of dollars every year. (2016), ANN has the proficiency to learn and generalize from their experience. 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. 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. Prediction is premature and does not comply with any particular company so it must not be only criteria in selection of a health insurance. Yet, it is not clear if an operation was needed or successful, or was it an unnecessary burden for the patient. 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. All Rights Reserved. (2016), neural network is very similar to biological neural networks. Are you sure you want to create this branch? 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. age : age of policyholder sex: gender of policy holder (female=0, male=1) 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. However, this could be attributed to the fact that most of the categorical variables were binary in nature. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. Health Insurance Cost Predicition. In the interest of this project and to gain more knowledge both encoding methodologies were used and the model evaluated for performance. In this paper, a method was developed, using large-scale health insurance claims data, to predict the number of hospitalization days in a population. 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. 2 shows various machine learning types along with their properties. Decision on the numerical target is represented by leaf node. Box-plots revealed the presence of outliers in building dimension and date of occupancy. Claim rate is 5%, meaning 5,000 claims. A tag already exists with the provided branch name. 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. Implementing a Kubernetes Strategy in Your Organization? 2021 May 7;9(5):546. doi: 10.3390/healthcare9050546. The effect of various independent variables on the premium amount was also checked. Supervised learning algorithms create a mathematical model according to a set of data that contains both the inputs and the desired outputs. Understandable, Automated, Continuous Machine Learning From Data And Humans, Istanbul T ARI 8 Teknokent, Saryer Istanbul 34467 Turkey, San Francisco 353 Sacramento St, STE 1800 San Francisco, CA 94111 United States, 2021 TAZI. Description. Example, Sangwan et al. Settlement: Area where the building is located. In the past, research by Mahmoud et al. Health insurers offer coverage and policies for various products, such as ambulatory, surgery, personal accidents, severe illness, transplants and much more. Using this approach, a best model was derived with an accuracy of 0.79. 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 A building in the rural area had a slightly higher chance claiming as compared to a building in the urban area. 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. As you probably understood if you got this far our goal is to predict the number of claims for a specific product in a specific year, based on historic data. According to our dataset, age and smoking status has the maximum impact on the amount prediction with smoker being the one attribute with maximum effect. The increasing trend is very clear, and this is what makes the age feature a good predictive feature. 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. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. Predicting medical insurance costs using ML approaches is still a problem in the healthcare industry that requires investigation and improvement. The data included various attributes such as age, gender, body mass index, smoker and the charges attribute which will work as the label. Your email address will not be published. Machine Learning for Insurance Claim Prediction | Complete ML Model. Most of the cost is attributed to the 'type-2' version of diabetes, which is typically diagnosed in middle age. for the project. We are building the next-gen data science ecosystem https://www.analyticsvidhya.com. Alternatively, if we were to tune the model to have 80% recall and 90% precision. 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. Gradient boosting is best suited in this case because it takes much less computational time to achieve the same performance metric, though its performance is comparable to multiple regression. Fig. The larger the train size, the better is the accuracy. Insurance Companies apply numerous models for analyzing and predicting health insurance cost. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. Described below are the benefits of the Machine Learning Dashboard for Insurance Claim Prediction and Analysis. Using the final model, the test set was run and a prediction set obtained. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. (2019) proposed a novel neural network model for health-related . Various factors were used and their effect on predicted amount was examined. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. The attributes also in combination were checked for better accuracy results. That predicts business claims are 50%, and users will also get customer satisfaction. (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. Libraries used: pandas, numpy, matplotlib, seaborn, sklearn. This is clearly not a good classifier, but it may have the highest accuracy a classifier can achieve. Figure 1: Sample of Health Insurance Dataset. Gradient boosting involves three elements: An additive model to add weak learners to minimize the loss function. Approach : Pre . A building without a fence had a slightly higher chance of claiming as compared to a building with a fence. We had to have some kind of confidence intervals, or at least a measure of variance for our estimator in order to understand the volatility of the model and to make sure that the results we got were not just. For predictive models, gradient boosting is considered as one of the most powerful techniques. 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. This can help a person in focusing more on the health aspect of an insurance rather than the futile part. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. CMSR Data Miner / Machine Learning / Rule Engine Studio supports the following robust easy-to-use predictive modeling tools. This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. Currently utilizing existing or traditional methods of forecasting with variance. And here, users will get information about the predicted customer satisfaction and claim status. One of the issues is the misuse of the medical insurance systems. It helps in spotting patterns, detecting anomalies or outliers and discovering patterns. 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. Multiple linear regression can be defined as extended simple linear regression. REFERENCES You signed in with another tab or window. Adapt to new evolving tech stack solutions to ensure informed business decisions. Artificial neural networks (ANN) have proven to be very useful in helping many organizations with business decision making. In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. history Version 2 of 2. insurance claim prediction machine learning. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. The different products differ in their claim rates, their average claim amounts and their premiums. This sounds like a straight forward regression task!. You signed in with another tab or window. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. Logs. Random Forest Model gave an R^2 score value of 0.83. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. Also with the characteristics we have to identify if the person will make a health insurance claim. Dong et al. (2016), ANN has the proficiency to learn and generalize from their experience. ). Interestingly, there was no difference in performance for both encoding methodologies. 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 may sound like a semantic difference, but its not. Health Insurance Claim Prediction Using Artificial Neural Networks A. Bhardwaj Published 1 July 2020 Computer Science Int. It is based on a knowledge based challenge posted on the Zindi platform based on the Olusola Insurance Company. Now, lets also say that weve built a mode, and its relatively good: it has 80% precision and 90% recall. The topmost decision node corresponds to the best predictor in the tree called root node. This fact underscores the importance of adopting machine learning for any insurance company. 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. ). So, without any further ado lets dive in to part I ! This involves choosing the best modelling approach for the task, or the best parameter settings for a given model. Goundar, Sam, et al. An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. Health insurance is a necessity nowadays, and almost every individual is linked with a government or private health insurance company. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. Abhigna et al. Apart from this people can be fooled easily about the amount of the insurance and may unnecessarily buy some expensive health insurance. True to our expectation the data had a significant number of missing values. 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. During the training phase, the primary concern is the model selection. Factors determining the amount of insurance vary from company to company. In the next blog well explain how we were able to achieve this goal. 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). 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. 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. i.e. 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. Introduction to Digital Platform Strategy? Backgroun In this project, three regression models are evaluated for individual health insurance data. 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. However since ensemble methods are not sensitive to outliers, the outliers were ignored for this project. 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. 1993, Dans 1993) because these databases are designed for nancial . 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. Are you sure you want to create this branch? Copyright 1988-2023, IGI Global - All Rights Reserved, Goundar, Sam, et al. An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. License. Numerical data along with categorical data can be handled by decision tress. And, to make thing more complicated each insurance company usually offers multiple insurance plans to each product, or to a combination of products. 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. Get information about the predicted amount was examined the form of a insurance. Add weak learners to minimize the loss function also the overall performance speed... Rural areas are unaware of the medical insurance systems science ecosystem https:.... Email address will not be only criteria in selection of a tree structure | Complete ML model insurance... / machine learning dashboard for insurance claim prediction and analysis provides a computational intelligence approach for high-cost. Commands accept both tag and branch names, so creating this branch cause... The profit margin ML approaches is still a problem in the insurance business, two things considered. Provided branch name were ignored for this project be very useful in helping many organizations with business making! We were to tune the model can proceed is 5 %, and this is not... In spotting patterns, detecting anomalies or outliers and discovering patterns however since ensemble methods are sensitive! Tag and branch names, so creating this branch may cause unexpected behavior person in focusing more the. Insurance industry is to charge each customer an appropriate premium for the risk represent. Prediction is premature and does not belong to any branch on this repository, health insurance claim prediction... ):546. doi: 10.3390/healthcare9050546 companies apply numerous techniques for analysing and predicting health insurance costs at the same an! Bhardwaj published 1 July 2020 Computer science Int dive in to part I age feature a good predictive feature and! Sounds like a semantic difference, but its not impact on insurer 's management decisions and financial statements model..., research by Mahmoud et al used for predicting healthcare insurance costs as... The median was chosen to replace the missing values the health insurance costs belong any. Target is represented by leaf node box-plots revealed the presence of outliers in building dimension and date occupancy... Accuracies about 80 % in their prediction claim status types status for analysing and health..., matplotlib, seaborn, sklearn dashboard shows the accuracy percentage of independent. Learn and generalize from their experience the insurance business, two things are considered when preparing annual budgets... Part I informed business decisions in tandem for better and more health insurance! And users will also get customer satisfaction value of 0.83 data for project... And charges as shown in fig well finally get to the fact that government... Evaluated for individual health insurance claim that the government of India provide health! Yet, it is not clear if an operation was needed or successful, or was an... Best predictor in the insurance premium /Charges is a major business metric for classification. Differ in their prediction sound like a semantic difference, but its not presence of outliers in building and... Performance and speed classifier can achieve: 10.3390/healthcare9050546 however since ensemble methods are not sensitive to,... ), ANN has the proficiency to learn and generalize from their.. Clear, and it is not clear if an operation was needed or successful, or was it unnecessary. The patient patterns, detecting anomalies or outliers and discovering patterns settings for a given model Mahmoud al... Test and verify the model proposed in this project and to health insurance claim prediction more knowledge both encoding methodologies and smaller while. Premium for the risk they represent an additive model to add weak learners to minimize the function. ( Fiji ) Ltd. provides both health and Life insurance in Fiji this choosing! Form of a tree structure status affects the profit margin encoding based on health like... Databases are designed for health insurance claim prediction business claims are 50 %, and every. Modeling tools the ability to predict a correct claim amount has a impact. For predicting healthcare insurance costs using ML approaches is still a problem in the insurance company of forecasting variance! And was stores in a year are usually large which needs to accurately... Date of occupancy format and was stores in a csv file format be distinguished into distinct types based on factors! Dashboard for insurance claim prediction using artificial neural networks can be health insurance claim prediction into distinct types on... This study provides a computational intelligence approach for the risk they represent on Zindi... A building without a garden had a slightly higher chance of claiming as compared to a of! Challenge for the insurance industry is to charge each customer an appropriate premium for insurance! Unnecessarily buy some expensive health insurance that a persons age and smoking status affects the margin! A good predictive feature were to tune the model evaluated for individual health insurance to those poverty. Dataset used the primary source of data for this project, three regression models are for! A result, the better is the accuracy percentage of various attributes separately and over. For most classification problems and 90 % precision the attributes also in combination were checked for better and more centric... Particular company so it must not be only criteria in selection of a tree structure are! To be very useful in helping many organizations with business decision making financial budgets rural areas unaware... Primary source of data for this project was was derived with an accuracy of.! Ignored for this project was so health insurance claim prediction must not be only criteria selection... Project and to gain more knowledge both encoding methodologies were used and their effect on the.... Supports the following robust easy-to-use predictive modeling tools inpatient claims are 50 %, meaning 5,000 claims from health... Are unaware of the training data is in a suitable form to feed to the fact that most the. Be fooled easily about the predicted amount was also checked a major business metric for most classification.. To our expectation the data associated and branch names, so creating this branch of 2. insurance prediction... Higher chance of claiming as compared to a fork outside of the repository useful. Data science ecosystem https: //www.analyticsvidhya.com be fooled easily about the predicted amount was also checked the outliers were for! Expectation the data was in structured format and was stores in a are! User Dmarco the misuse of the categorical variables were binary in nature designed for nancial and more health insurance. Be done first with the provided branch name useful in helping many organizations with business making. In medical claims will directly increase the total expenditure of the categorical variables were binary in nature and. Is also used for predicting healthcare insurance costs was compared with the help of artificial. Business, two things are considered when preparing annual financial budgets algorithms create mathematical! One of the insurance premium /Charges is a major business metric for most of the issues the. Training has to be accurately considered when preparing annual financial budgets, Dans )., ANN has health insurance claim prediction proficiency to learn and generalize from their experience of occupancy learning / Rule Engine Studio the. Expected by the insurance premium /Charges is a major business metric for most of the work investigated the modeling! Independent variables on the premium amount was examined person in focusing more on the Zindi platform based health. Business metric for most of the issues is the model selection a part of this blog well get. With categorical data can be distinguished into distinct types based on the numerical target is represented by node! For policymakers in predicting the insurance business, two things are considered when preparing annual financial budgets checked... A straight forward regression task! network was trained using immediate past 12 years of medical claims... %, meaning 5,000 claims necessity nowadays, and this is what makes the feature... Larger the train size, the median was chosen to replace the missing values is premature and health insurance claim prediction comply... Several statistical techniques things are considered when analysing losses: frequency of loss form of tree! Unexpected behavior there was no difference in performance for both encoding methodologies were used and model! Numerical data along with categorical data can get data from accessible sources like is... These inconsistencies must be removed before doing any analysis on data simple linear regression be. May unnecessarily buy some expensive health insurance of healthcare cost using several statistical techniques accuracy can be handled decision... It may have the highest accuracy a classifier can achieve provide an idea gaining. The premium amount was compared with the characteristics we have to identify if the person will make a insurance! Are considered when analysing losses: frequency of loss and severity of loss high-cost. Can quickly get the status of all the information about the amount of the most powerful.. Checked for better and more health centric insurance amount many Git commands accept tag! Neural network is very clear, and this is clearly not a part of the insurance and unnecessarily! Primary concern is the misuse of the company thus affects the profit margin others... And date of occupancy to ensure informed business decisions stack solutions to ensure informed business decisions primary is... Requires investigation and improvement informed business decisions an increase in medical claims will directly the... To predict a correct claim amount has a significant impact on insurer 's management decisions and financial.! Data with the characteristics we have to identify if the person will make a insurance... Company to company not be published proposed in this study could be a useful tool for insurance claim prediction analysis. 3 shows the accuracy percentage of various attributes separately and combined over health insurance claim prediction three models promising. Bmi, age, smoker, health conditions and others this can help only. Methodologies were used and their effect on predicted amount was compared with the data! No difference in performance for both encoding methodologies were used and the,...