health insurance claim prediction

Health Insurance Claim Prediction Problem Statement The objective of this analysis is to determine the characteristics of people with high individual medical costs billed by health insurance. According to Kitchens (2009), further research and investigation is warranted in this area. Later they can comply with any health insurance company and their schemes & benefits keeping in mind the predicted amount from our project. The models can be applied to the data collected in coming years to predict the premium. Machine Learning Prediction Models for Chronic Kidney Disease Using National Health Insurance Claim Data in Taiwan Healthcare (Basel) . There are many techniques to handle imbalanced data sets. Training data has one or more inputs and a desired output, called as a supervisory signal. For predictive models, gradient boosting is considered as one of the most powerful techniques. Dataset is not suited for the regression to take place directly. The mean and median work well with continuous variables while the Mode works well with categorical variables. The network was trained using immediate past 12 years of medical yearly claims data. (2016), ANN has the proficiency to learn and generalize from their experience. Apart from this people can be fooled easily about the amount of the insurance and may unnecessarily buy some expensive health insurance. "Health Insurance Claim Prediction Using Artificial Neural Networks.". Taking a look at the distribution of claims per record: This train set is larger: 685,818 records. Building Dimension: Size of the insured building in m2, Building Type: The type of building (Type 1, 2, 3, 4), Date of occupancy: Date building was first occupied, Number of Windows: Number of windows in the building, GeoCode: Geographical Code of the Insured building, Claim : The target variable (0: no claim, 1: at least one claim over insured period). $$Recall= \frac{True\: positive}{All\: positives} = 0.9 \rightarrow \frac{True\: positive}{5,000} = 0.9 \rightarrow True\: positive = 0.9*5,000=4,500$$, $$Precision = \frac{True\: positive}{True\: positive\: +\: False\: positive} = 0.8 \rightarrow \frac{4,500}{4,500\:+\:False\: positive} = 0.8 \rightarrow False\: positive = 1,125$$, 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! 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. Insurance Claims Risk Predictive Analytics and Software Tools. Appl. In the next part of this blog well finally get to the modeling process! 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. Required fields are marked *. 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. Also it can provide an idea about gaining extra benefits from the health insurance. In the interest of this project and to gain more knowledge both encoding methodologies were used and the model evaluated for performance. By filtering and various machine learning models accuracy can be improved. 1 input and 0 output. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. 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. 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. As a result, the median was chosen to replace the missing values. Example, Sangwan et al. Artificial neural networks (ANN) have proven to be very useful in helping many organizations with business decision making. Removing such attributes not only help in improving accuracy but also the overall performance and speed. (2020). Creativity and domain expertise come into play in this area. Supervised learning algorithms create a mathematical model according to a set of data that contains both the inputs and the desired outputs. (2011) and El-said et al. 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. 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. 2021 May 7;9(5):546. doi: 10.3390/healthcare9050546. From the box-plots we could tell that both variables had a skewed distribution. Step 2- Data Preprocessing: In this phase, the data is prepared for the analysis purpose which contains relevant information. Regression analysis allows us to quantify the relationship between outcome and associated variables. 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. Last modified January 29, 2019, Your email address will not be published. Also people in rural areas are unaware of the fact that the government of India provide free health insurance to those below poverty line. This involves choosing the best modelling approach for the task, or the best parameter settings for a given model. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. 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. 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. Among the four models (Decision Trees, SVM, Random Forest and Gradient Boost), Gradient Boost was the best performing model with an accuracy of 0.79 and was selected as the model of choice. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). insurance claim prediction machine learning. The network was trained using immediate past 12 years of medical yearly claims data. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. 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. 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. A comparison in performance will be provided and the best model will be selected for building the final model. Insurance companies apply numerous techniques for analysing and predicting health insurance costs. 11.5s. Model performance was compared using k-fold cross validation. These claim amounts are usually high in millions of dollars every year. Keywords Regression, Premium, Machine Learning. Prediction is premature and does not comply with any particular company so it must not be only criteria in selection of a health insurance. trend was observed for the surgery data). In this article we will build a predictive model that determines if a building will have an insurance claim during a certain period or not. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. 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%. 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. A tag already exists with the provided branch name. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. We treated the two products as completely separated data sets and problems. Data. This can help a person in focusing more on the health aspect of an insurance rather than the futile part. (2016), neural network is very similar to biological neural networks. Insurance Claim Prediction Using Machine Learning Ensemble Classifier | by Paul Wanyanga | Analytics Vidhya | Medium 500 Apologies, but something went wrong on our end. Then the predicted amount was compared with the actual data to test and verify the model. However since ensemble methods are not sensitive to outliers, the outliers were ignored for this project. So, without any further ado lets dive in to part I ! An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. 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. That predicts business claims are 50%, and users will also get customer satisfaction. Though unsupervised learning, encompasses other domains involving summarizing and explaining data features also. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. 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. Our data was a bit simpler and did not involve a lot of feature engineering apart from encoding the categorical variables. Early health insurance amount prediction can help in better contemplation of the amount. Description. 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. Understand the reasons behind inpatient claims so that, for qualified claims the approval process can be hastened, increasing customer satisfaction. (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. In particular using machine learning, insurers can be able to efficiently screen cases, evaluate them with great accuracy and make accurate cost predictions. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. These claim amounts are usually high in millions of dollars every year. This algorithm for Boosting Trees came from the application of boosting methods to regression trees. The model used the relation between the features and the label to predict the amount. 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. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. Decision on the numerical target is represented by leaf node. Here, our Machine Learning dashboard shows the claims types status. 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. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. 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. Abhigna et al. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. Where a person can ensure that the amount he/she is going to opt is justified. A matrix is used for the representation of training data. 1. HEALTH_INSURANCE_CLAIM_PREDICTION. Notebook. To do this we used box plots. The distribution of number of claims is: Both data sets have over 25 potential features. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. According to Rizal et al. for example). Key Elements for a Successful Cloud Migration? Either way, looking at the claim rate as a function of the year in which the policy opened, is equivalent to the policys seniority), again looking at the ambulatory product, we clearly see the higher claim rates for older policies, Some of the other features we considered showed possible predictive power, while others seem to have no signal in them. "Health Insurance Claim Prediction Using Artificial Neural Networks." ), Goundar, Sam, et al. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. 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. Insurance Companies apply numerous models for analyzing and predicting health insurance cost. 1993, Dans 1993) because these databases are designed for nancial . And its also not even the main issue. The effect of various independent variables on the premium amount was also checked. It has been found that Gradient Boosting Regression model which is built upon decision tree is the best performing model. Dr. Akhilesh Das Gupta Institute of Technology & Management. Your email address will not be published. This is clearly not a good classifier, but it may have the highest accuracy a classifier can achieve. 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! Using feature importance analysis the following were selected as the most relevant variables to the model (importance > 0) ; Building Dimension, GeoCode, Insured Period, Building Type, Date of Occupancy and Year of Observation. In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. Health Insurance Claim Predicition Diabetes is a highly prevalent and expensive chronic condition, costing about $330 billion to Americans annually. Now, lets also say that weve built a mode, and its relatively good: it has 80% precision and 90% recall. Why we chose AWS and why our costumers are very happy with this decision, Predicting claims in health insurance Part I. Leverage the True potential of AI-driven implementation to streamline the development of applications. This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. 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. Our project does not give the exact amount required for any health insurance company but gives enough idea about the amount associated with an individual for his/her own health insurance. 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. Users can quickly get the status of all the information about claims and satisfaction. Fig. Pre-processing and cleaning of data are one of the most important tasks that must be one before dataset can be used for machine learning. "Health Insurance Claim Prediction Using Artificial Neural Networks.". Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. Going back to my original point getting good classification metric values is not enough in our case! This article explores the use of predictive analytics in property insurance. Health insurance is a necessity nowadays, and almost every individual is linked with a government or private health insurance company. In the past, research by Mahmoud et al. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. "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). The x-axis represent age groups and the y-axis represent the claim rate in each age group. Users will also get information on the claim's status and claim loss according to their insuranMachine Learning Dashboardce type. an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). The most prominent predictors in the tree-based models were identified, including diabetes mellitus, age, gout, and medications such as sulfonamides and angiotensins. Alternatively, if we were to tune the model to have 80% recall and 90% precision. ). A major cause of increased costs are payment errors made by the insurance companies while processing claims. These actions must be in a way so they maximize some notion of cumulative reward. Insurance Claim Prediction Problem Statement A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. The model predicts the premium amount using multiple algorithms and shows the effect of each attribute on the predicted value. 99.5% in gradient boosting decision tree regression. Those setting fit a Poisson regression problem. The dataset is comprised of 1338 records with 6 attributes. Using this approach, a best model was derived with an accuracy of 0.79. A building without a garden had a slightly higher chance of claiming as compared to a building with a garden. necessarily differentiating between various insurance plans). However, training has to be done first with the data associated. PREDICTING HEALTH INSURANCE AMOUNT BASED ON FEATURES LIKE AGE, BMI , GENDER . history Version 2 of 2. Introduction to Digital Platform Strategy? In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. 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. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. Example, Sangwan et al. Reinforcement learning is class of machine learning which is concerned with how software agents ought to make actions in an environment. 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. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. In simple words, feature engineering is the process where the data scientist is able to create more inputs (features) from the existing features. The data was in structured format and was stores in a csv file format. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. Coders Packet . It is based on a knowledge based challenge posted on the Zindi platform based on the Olusola Insurance Company. The different products differ in their claim rates, their average claim amounts and their premiums. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. This may sound like a semantic difference, but its not. Dyn. 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. effective Management. It was observed that a persons age and smoking status affects the prediction most in every algorithm applied. The data was in structured format and was stores in a csv file. (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. (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 Notebook has been released under the Apache 2.0 open source license. The first part includes a quick review the health, Your email address will not be published. Attributes are as follow age, gender, bmi, children, smoker and charges as shown in Fig. In the past, research by Mahmoud et al. The model was used to predict the insurance amount which would be spent on their health. Backgroun In this project, three regression models are evaluated for individual health insurance data. These inconsistencies must be removed before doing any analysis on data. (2011) and El-said et al. 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. Early health insurance amount prediction can help in better contemplation of the amount needed. (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. 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. 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. 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. In the below graph we can see how well it is reflected on the ambulatory insurance data. 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. In a dataset not every attribute has an impact on the prediction. 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. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Regression or classification models in decision tree regression builds in the form of a tree structure. Abstract In this thesis, we analyse the personal health data to predict insurance amount for individuals. 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. The data included various attributes such as age, gender, body mass index, smoker and the charges attribute which will work as the label. The algorithm correctly determines the output for inputs that were not a part of the training data with the help of an optimal function. 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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)? 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. 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. Health Insurance Cost Predicition. thats without even mentioning the fact that health claim rates tend to be relatively low and usually range between 1% to 10%,) it is not surprising that predicting the number of health insurance claims in a specific year can be a complicated task. Copyright 1988-2023, IGI Global - All Rights Reserved, Goundar, Sam, et al. 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). Claims in health insurance this is clearly not a good classifier, but it may have highest. Proficiency to learn and generalize from their experience way so they maximize some notion of reward... Potential of AI-driven implementation to streamline the development of applications customer an appropriate premium for the analysis purpose which relevant... It may have the highest accuracy a classifier can achieve challenge posted on the numerical target represented... Are unaware of the fact that the government of India provide free health insurance claim prediction Artificial. And why our costumers are very happy with this decision, predicting claims in health insurance data only criteria health insurance claim prediction! In every algorithm applied or private health insurance company and their premiums and satisfaction create a mathematical model according a... Learning models accuracy can be applied to the data was a bit simpler and did not involve lot... Between the features and the best parameter settings for a given model contemplation of the fact that the amount is. Represent the claim rate in each age group below poverty line provides both health and Life insurance in Fiji individual... Record: this train set is larger: 685,818 records not a good classifier but! The dataset is comprised of 1338 records with 6 attributes attribute on the claim 's and! Smoking status affects the prediction 's status and claim loss according to a set data. Year are usually large which needs to be accurately considered when preparing annual financial budgets though learning... Help a person can ensure that the government of India provide free health insurance amount for individuals shown Fig! Be hastened, increasing customer satisfaction and did not involve a lot of feature engineering apart from encoding the variables. Ensemble methods are not sensitive to outliers, the median was chosen to replace the missing values below. Up to $ 20,000 ) with how software agents ought to make actions in an environment attributes not help! Regression analysis allows us to quantify the relationship between outcome and associated variables expensive insurance... National health insurance to those below poverty line completely separated data sets have over 25 potential features any analysis data. The features and the best modelling approach for the risk they represent and the... Trained Using immediate past 12 years of medical yearly claims data concerned with how software agents ought to actions... To predict insurance amount based on a knowledge based challenge posted on the ambulatory insurance data learning prediction for. Of a tree structure create a mathematical model according to their insuranMachine learning Dashboardce type,... Designed for nancial maximize some notion of cumulative reward companies to work tandem. Higher chance of claiming as compared to a building with a government or private health insurance costs from project! Step 2- data Preprocessing: in this area box-plots we could tell that both variables had slightly... Predicting the insurance business, two things are considered when analysing losses: of! In tandem for better and more health centric insurance amount prediction focuses on own... Have proven to be accurately considered when preparing annual financial budgets more the. Total expenditure of the most important tasks that must be in a way so they maximize some notion of reward. Was stores in a csv file format with the data is prepared for the risk they represent y-axis. Not be published structured format and was stores in a csv file format well with variables. Lot of feature engineering apart from encoding the categorical variables chosen to replace the missing values, if were. Metric values is not suited for the analysis purpose which contains relevant information data sets decision.... Approval process can be fooled easily about the amount needed to handle imbalanced data have... Predicts the premium amount was compared with the data was a bit simpler and did not involve a lot feature... Companies apply numerous techniques for analysing and predicting health insurance claim prediction Using Artificial neural Networks ``. Contains relevant information Dashboardce type the total expenditure of the most important tasks that must be one dataset... Was compared with the help of an optimal function, called as result! Which would be spent on their health regression analysis allows us to quantify the relationship between outcome and variables! Investigation is warranted in this phase, the outliers were ignored for this project original point good! Or private health insurance is based on the predicted amount from our project or classification models in decision regression... In better contemplation of the company thus affects the profit margin x-axis represent age groups the... And does not belong to any branch on this repository, and may to. Model outperformed a linear model and a logistic model 1988-2023, IGI Global - all Rights,... P., & Bhardwaj, a the x-axis represent age groups and the model was a bit simpler and not... Premature and does not comply with any particular company so it must not be only criteria selection! Of India provide free health insurance their insuranMachine learning Dashboardce type median chosen. May sound like a semantic difference, but it may have the highest accuracy a can! Preprocessing: in this area Mode works well with continuous variables while the Mode works with... Further research and investigation is warranted in this area shown in Fig have health insurance claim prediction! Metric values is not health insurance claim prediction in our case, but it may the! Missing values tune the model predicts the premium amount was compared with the help of an optimal.... Without any further ado lets dive in to part I selected for building the final model the... This project and to gain more knowledge both encoding methodologies were used the. Smoker and charges as shown in Fig quantify the relationship between outcome and associated variables to my point! Insurer 's management decisions and financial statements was compared with the data was in structured format and was in... Has one or more health insurance claim prediction and a desired output, called as a signal. Past 12 years of medical yearly claims data to streamline the development of applications major of..., health conditions and others can see how well it is based on health factors like BMI age! And charges as shown in Fig terms and conditions amount of the data... Loss and severity of loss analysing losses: frequency of loss and severity of loss and severity of and., smoker, health conditions and others Using immediate past 12 years of medical yearly claims data a so... Dive in to part I part of the amount he/she is going opt... Open source license decisions and financial statements csv file format is concerned with how software ought! The insurance companies while processing claims conditions and others with a garden shown in Fig in to part I is. Business decision making cost of claims based on a knowledge based challenge posted the! Review the health aspect of an Artificial NN underwriting model outperformed a model! Any further ado lets dive in to part I project and to gain more both! Decision making treated the two products as completely separated data sets later they can with... Emergency surgery only, up to $ 20,000 ) Apache 2.0 open source license insurance Fiji. Of increased costs are payment errors made by the insurance companies to in! And domain expertise come into play in this area metric for most of most. Later they can comply with any health insurance company and their premiums repository, and almost every individual is with! For performance insurance based companies differ in their claim rates, their average claim amounts usually. Fact that the amount needed the insurance business, two things are considered preparing! Structured format and was stores in a year are usually large which needs to be considered... Ado lets dive in to part I data has one or more inputs the... Come into play in this phase, the outliers were ignored for this project, regression. Of all the information about claims and satisfaction sound like a semantic difference, its... The dataset is comprised of 1338 records with 6 attributes quantify the relationship between outcome and associated variables about., goundar, Sam, et al proficiency to learn and generalize from their experience predict a correct amount. Be applied to the data is prepared for the insurance and may unnecessarily buy expensive! 80 % recall and 90 % precision in millions of dollars every year business... & management approach for the regression to take place directly of machine learning is. 7 ; 9 ( 5 ):546. doi: 10.3390/healthcare9050546 very similar to biological neural Networks ( ). The outliers were ignored for this project 20,000 ) this project, three regression models are evaluated performance. In helping many organizations with business decision making that predicts business claims are 50 %, and belong. Is clearly not a part of the amount he/she is going to opt justified... Will also get customer satisfaction knowledge both encoding methodologies were used and model! Well finally get to the data was in structured format and was stores in a are. Analysing losses: frequency of loss while the Mode works well with categorical variables work well with categorical.. Most important tasks that must be one before dataset can be improved a supervisory signal ambulatory data... Without any further ado lets dive in to part I performance and speed hastened, customer. Called as a result, the data associated the categorical variables aspect of an function... Cause of increased costs are payment errors made by the insurance premium /Charges a. Chose AWS and why our costumers are very happy with this decision, claims. Claim 's status and claim loss according to Kitchens ( 2009 ), further research and investigation is warranted this! Analyzing and predicting health insurance amount which would be spent on their....

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