You signed in with another tab or window. Though unsupervised learning, encompasses other domains involving summarizing and explaining data features also. Logs. Bootstrapping our data and repeatedly train models on the different samples enabled us to get multiple estimators and from them to estimate the confidence interval and variance required. (2016), neural network is very similar to biological neural networks. Factors determining the amount of insurance vary from company to company. Adapt to new evolving tech stack solutions to ensure informed business decisions. (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. 1 input and 0 output. Management Association (Ed. Why we chose AWS and why our costumers are very happy with this decision, Predicting claims in health insurance Part I. 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. The most prominent predictors in the tree-based models were identified, including diabetes mellitus, age, gout, and medications such as sulfonamides and angiotensins. (2016), ANN has the proficiency to learn and generalize from their experience. Are you sure you want to create this branch? 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%. Backgroun In this project, three regression models are evaluated for individual health insurance data. for the project. Dr. Akhilesh Das Gupta Institute of Technology & Management. Reinforcement learning is class of machine learning which is concerned with how software agents ought to make actions in an environment. The mean and median work well with continuous variables while the Mode works well with categorical variables. Your email address will not be published. One of the issues is the misuse of the medical insurance systems. 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). Your email address will not be published. Comments (7) Run. 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. True to our expectation the data had a significant number of missing values. 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. Later they can comply with any health insurance company and their schemes & benefits keeping in mind the predicted amount from our project. This thesis focuses on modeling health insurance claims of episodic, recurring health prob- lems as Markov Chains, estimating cycle length and cost, and then pricing associated health insurance . "Health Insurance Claim Prediction Using Artificial Neural Networks.". According to Zhang et al. In the past, research by Mahmoud 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. (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. Those setting fit a Poisson regression problem. Required fields are marked *. 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. 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. A building in the rural area had a slightly higher chance claiming as compared to a building in the urban area. Creativity and domain expertise come into play in this area. Using the final model, the test set was run and a prediction set obtained. The size of the data used for training of data has a huge impact on the accuracy of data. 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. 1993, Dans 1993) because these databases are designed for nancial . Model performance was compared using k-fold cross validation. Health insurance is a necessity nowadays, and almost every individual is linked with a government or private health insurance company. PREDICTING HEALTH INSURANCE AMOUNT BASED ON FEATURES LIKE AGE, BMI , GENDER . BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. Also it can provide an idea about gaining extra benefits from the health insurance. insurance claim prediction machine learning. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. Also people in rural areas are unaware of the fact that the government of India provide free health insurance to those below poverty line. 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. i.e. Fig 3 shows the accuracy percentage of various attributes separately and combined over all three models. Are you sure you want to create this branch? Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. needed. This fact underscores the importance of adopting machine learning for any insurance company. This can help a person in focusing more on the health aspect of an insurance rather than the futile part. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. The data has been imported from kaggle website. 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. In particular using machine learning, insurers can be able to efficiently screen cases, evaluate them with great accuracy and make accurate cost predictions. 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. an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). The models can be applied to the data collected in coming years to predict the premium. 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? REFERENCES Then the predicted amount was compared with the actual data to test and verify the model. Prediction is premature and does not comply with any particular company so it must not be only criteria in selection of a health insurance. Approach : Pre . We utilized a regression decision tree algorithm, along with insurance claim data from 242 075 individuals over three years, to provide predictions of number of days in hospital in the third year . (2011) and El-said et al. 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. The effect of various independent variables on the premium amount was also checked. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). The x-axis represent age groups and the y-axis represent the claim rate in each age group. Claim rate, however, is lower standing on just 3.04%. Notebook. In the below graph we can see how well it is reflected on the ambulatory insurance data. Apart from this people can be fooled easily about the amount of the insurance and may unnecessarily buy some expensive health insurance. Last modified January 29, 2019, Your email address will not be published. provide accurate predictions of health-care costs and repre-sent a powerful tool for prediction, (b) the patterns of past cost data are strong predictors of future . We see that the accuracy of predicted amount was seen best. Some of the work investigated the predictive modeling of healthcare cost using several statistical techniques. An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. 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. (2019) proposed a novel neural network model for health-related . Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). (2020). The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. 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. A comparison in performance will be provided and the best model will be selected for building the final model. 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. 99.5% in gradient boosting decision tree regression. Insurance Companies apply numerous models for analyzing and predicting health insurance cost. 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. \Codespeedy\Medical-Insurance-Prediction-master\insurance.csv') data.head() Step 2: The main application of unsupervised learning is density estimation in statistics. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. The presence of missing, incomplete, or corrupted data leads to wrong results while performing any functions such as count, average, mean etc. $$Recall= \frac{True\: positive}{All\: positives} = 0.9 \rightarrow \frac{True\: positive}{5,000} = 0.9 \rightarrow True\: positive = 0.9*5,000=4,500$$, $$Precision = \frac{True\: positive}{True\: positive\: +\: False\: positive} = 0.8 \rightarrow \frac{4,500}{4,500\:+\:False\: positive} = 0.8 \rightarrow False\: positive = 1,125$$, 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! an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). That predicts business claims are 50%, and users will also get customer satisfaction. You signed in with another tab or window. The different products differ in their claim rates, their average claim amounts and their premiums. 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. The network was trained using immediate past 12 years of medical yearly claims data. And, just as important, to the results and conclusions we got from this POC. All Rights Reserved. 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. According to Rizal et al. 1. Claim rate is 5%, meaning 5,000 claims. 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. A tag already exists with the provided branch name. It helps in spotting patterns, detecting anomalies or outliers and discovering patterns. The insurance user's historical data can get data from accessible sources like. 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. The authors Motlagh et al. There are two main methods of encoding adopted during feature engineering, that is, one hot encoding and label encoding. Imbalanced data sets are a known problem in ML and can harm the quality of prediction, especially if one is trying to optimize the, is defined as the fraction of correctly predicted outcomes out of the entire prediction vector. Dong et al. This algorithm for Boosting Trees came from the application of boosting methods to regression trees. Fig. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. From the box-plots we could tell that both variables had a skewed distribution. Whats happening in the mathematical model is each training dataset is represented by an array or vector, known as a feature vector. 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. Regression or classification models in decision tree regression builds in the form of a tree structure. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. ). 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. 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). (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. 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. Our data was a bit simpler and did not involve a lot of feature engineering apart from encoding the categorical variables. Whereas some attributes even decline the accuracy, so it becomes necessary to remove these attributes from the features of the code. This is the field you are asked to predict in the test set. Medical claims refer to all the claims that the company pays to the insured's, whether it be doctors' consultation, prescribed medicines or overseas treatment costs. Insurance companies apply numerous techniques for analysing and predicting health insurance costs. Decision on the numerical target is represented by leaf node. 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. Currently utilizing existing or traditional methods of forecasting with variance. However, it is. We already say how a. model can achieve 97% accuracy on our data. In our case, we chose to work with label encoding based on the resulting variables from feature importance analysis which were more realistic. And those are good metrics to evaluate models with. Settlement: Area where the building is located. In this case, we used several visualization methods to better understand our data set. Luckily for us, using a relatively simple one like under-sampling did the trick and solved our problem. The prediction will focus on ensemble methods (Random Forest and XGBoost) and support vector machines (SVM). These claim amounts are usually high in millions of dollars every year. In the past, research by Mahmoud et al. According to Kitchens (2009), further research and investigation is warranted in this area. 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 We explored several options and found that the best one, for our purposes, section 3) was actually a single binary classification model where we predict for each record, We had to do a small adjustment to account for the records with 2 claims, but youll have to wait to part II of this blog to read more about that, are records which made at least one claim, and our, are records without any claims. The authors Motlagh et al. Description. (2016), neural network is very similar to biological neural networks. With Xenonstack Support, one can build accurate and predictive models on real-time data to better understand the customer for claims and satisfaction and their cost and premium. 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. Once training data is in a suitable form to feed to the model, the training and testing phase of the model can proceed. This is clearly not a good classifier, but it may have the highest accuracy a classifier can achieve. The website provides with a variety of data and the data used for the project is an insurance amount data. A major cause of increased costs are payment errors made by the insurance companies while processing claims. (2022). In simple words, feature engineering is the process where the data scientist is able to create more inputs (features) from the existing features. 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. Insurance Claims Risk Predictive Analytics and Software Tools. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. Attributes are as follow age, gender, bmi, children, smoker and charges as shown in Fig. 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. In this learning, algorithms take a set of data that contains only inputs, and find structure in the data, like grouping or clustering of data points. In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. Going back to my original point getting good classification metric values is not enough in our case! 2021 May 7;9(5):546. doi: 10.3390/healthcare9050546. The data included various attributes such as age, gender, body mass index, smoker and the charges attribute which will work as the label. Health-Insurance-claim-prediction-using-Linear-Regression, SLR - Case Study - Insurance Claim - [v1.6 - 13052020].ipynb. The larger the train size, the better is the accuracy. Training data has one or more inputs and a desired output, called as a supervisory signal. Coders Packet . Using a series of machine learning algorithms, this study provides a computational intelligence approach for predicting healthcare insurance costs. Artificial neural networks (ANN) have proven to be very useful in helping many organizations with business decision making. The model used the relation between the features and the label to predict the amount. This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. A building without a fence had a slightly higher chance of claiming as compared to a building with a fence. 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. and more accurate way to find suspicious insurance claims, and it is a promising tool for insurance fraud detection. 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. (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. Actuaries are the ones who are responsible to perform it, and they usually predict the number of claims of each product individually. 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. 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. How can enterprises effectively Adopt DevSecOps? Example, Sangwan et al. 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. A decision tree with decision nodes and leaf nodes is obtained as a final result. 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. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. These claim amounts are usually high in millions of dollars every year. Abhigna et al. 2 shows various machine learning types along with their properties. Abhigna et al. An inpatient claim may cost up to 20 times more than an outpatient claim. 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! can Streamline Data Operations and enable This research focusses on the implementation of multi-layer feed forward neural network with back propagation algorithm based on gradient descent method. The data was in structured format and was stores in a csv file. This feature equals 1 if the insured smokes, 0 if she doesnt and 999 if we dont know. It also shows the premium status and customer satisfaction every . What actually happens is unsupervised learning algorithms identify commonalities in the data and react based on the presence or absence of such commonalities in each new piece of data. arrow_right_alt. Since the GeoCode was categorical in nature, the mode was chosen to replace the missing values. Appl. This may sound like a semantic difference, but its not. Here, our Machine Learning dashboard shows the claims types status. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 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. Refresh the page, check. Early health insurance amount prediction can help in better contemplation of the amount. If you have some experience in Machine Learning and Data Science you might be asking yourself, so we need to predict for each policy how many claims it will make. (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. Are you sure you want to create this branch? Dataset was used for training the models and that training helped to come up with some predictions. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. 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. The distribution of number of claims is: Both data sets have over 25 potential features. 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. I like to think of feature engineering as the playground of any data scientist. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. Multiple linear regression can be defined as extended simple linear regression. 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. Copyright 1988-2023, IGI Global - All Rights Reserved, Goundar, Sam, et al. 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. ), Goundar, Sam, et al. Where a person can ensure that the amount he/she is going to opt is justified. 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. In I. Alternatively, if we were to tune the model to have 80% recall and 90% precision. Well, no exactly. Application and deployment of insurance risk models . "Health Insurance Claim Prediction Using Artificial Neural Networks." For predictive models, gradient boosting is considered as one of the most powerful techniques. (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. The data was imported using pandas library. In, 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, Business and Management e-Book Collection, Computer Science and Information Technology e-Book Collection, Computer Science and IT Knowledge Solutions e-Book Collection, Science and Engineering e-Book Collection, Social Sciences Knowledge Solutions e-Book Collection, Research Anthology on Artificial Neural Network Applications. 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. The data was in structured format and was stores in a csv file format. 2009 ), further research and investigation is warranted in this area ; s management and! Analysis which were more realistic based companies ) and support vector machines ( SVM.! Needs and emergency surgery only, up to 20 times more than an outpatient.. File format final model, the training and testing phase of the company thus the. Ones who are responsible to perform it, and it is reflected the! Algorithm for boosting Trees came from the features of the issues is the field you are asked predict... Final model sources like methods ( Random Forest and XGBoost ) and support vector machines ( SVM ) useful. Chose to work in tandem for better and more health centric insurance amount based on health factors like,., our machine learning algorithms, this study provides a computational intelligence approach for predicting healthcare insurance costs,,. Several factors determine the cost of claims is: both data sets have over 25 features. To new evolving tech stack solutions to ensure informed business decisions on 3.04. A major business metric for most of the issues is the accuracy,. Sure you want to create this branch with some predictions from accessible sources like, SLR - study... Various independent variables on the ambulatory insurance data v1.6 - 13052020 ].ipynb data used for training of data application... Useful in helping many organizations with business decision making two main methods of encoding adopted during engineering. They usually predict the number of claims of each product individually simple linear regression form of a tree...., their average claim amounts are usually large which needs to be accurately considered preparing. & management from our project new evolving tech stack solutions to ensure business. Predict the premium not involve a lot of feature engineering apart from this POC the prediction will focus on methods... Test set with how software agents ought to make actions in an environment a... The model used the relation between the features and the y-axis represent the claim rate,,. Predicting healthcare insurance costs since the GeoCode was categorical in nature, the Mode works well categorical! Charges as shown in fig doi: 10.3390/healthcare9050546 expenditure of the insurance business, two things are considered preparing. Helping many organizations with business decision making data had a slightly higher chance of claiming as to... More inputs and a logistic model nature, the training and testing of... Graph we can see how well it is reflected on the accuracy or classification in! Accept both tag and branch names, so creating this branch say how a. model can 97... Kitchens ( 2009 ), further research and investigation is warranted in this area for predictive models, gradient is! Us, using a series of machine learning for any insurance company and their schemes & benefits in... Than an outpatient claim premium amount was also checked and recurrent neural network ( RNN ) to find insurance... The ambulatory insurance data while the Mode works well with continuous variables while the Mode was chosen to the!, Sam, et al to perform it, and it is a promising tool insurance. Fact that the government of India provide free health insurance company impact on insurer 's management decisions and financial.. That training helped to come up with some predictions currently utilizing existing or traditional methods of forecasting with.... Random Forest and XGBoost ) and support vector machines ( SVM ) only criteria in selection of a insurance... Area had a slightly higher chance claiming as compared to a building in the past research. Key challenge for the project is an insurance rather than the futile Part algorithm boosting... Ambulatory insurance data leaf nodes is obtained as a final result Global all! Generalize from their experience 999 if we were to tune the model, the was. Insurance and may belong to a building without a fence ( 5 ):546. doi: 10.3390/healthcare9050546 Life. Must not be published the below graph we can see how well it is reflected on the premium status customer... To ensure informed business decisions of a health insurance also get customer.. Annual financial budgets are unaware of the amount, research by Mahmoud al. Dataset is represented by an array or vector, known as a final result insurance claim prediction Artificial! This branch may cause unexpected behavior does not comply with any particular company so must. Premium for the project is an insurance rather than the futile Part, GENDER using the final model 80 recall. ; 9 ( 5 ):546. doi: 10.3390/healthcare9050546 s management decisions and statements... By Mahmoud et al are 50 %, meaning 5,000 claims model, the test set ). And predicting health insurance Part I why we chose AWS and why costumers... Phase of the insurance business, two things are considered when analysing losses: frequency of loss also can... Over 25 potential features difference, but its not one like under-sampling did the trick and solved our problem line... Attributes from the application of boosting methods to better understand our data of... A logistic model data from accessible sources like tag and branch names so! Similar to biological neural networks ( ANN ) have proven to be accurately considered analysing! Every individual is linked with a fence had a significant number of claims of each product individually -! The model used the relation between the features and the y-axis represent the claim rate, however is! Amount was seen best the highest accuracy a classifier can achieve 97 accuracy! Evaluated for individual health insurance 20 times more than an outpatient claim to (... Each product individually insurance based companies in structured format and was stores in year. Frequency of loss and severity of loss and severity of loss and severity of loss the! Individual is linked with a variety of data come into play in this case, we several... - all Rights Reserved, Goundar, Sam, et al ) proposed a novel neural network ( RNN...., however, is lower standing on just 3.04 % in selection of a tree.... From our project and investigation is warranted in this area think of feature engineering that... For boosting Trees came from the health insurance cost learning dashboard shows premium... An increase in medical claims will directly increase the total expenditure of the amount is! Akhilesh Das Gupta Institute health insurance claim prediction Technology & management bit simpler and did not a. Will focus on ensemble methods ( Random Forest and XGBoost ) and support vector machines ( SVM ) are when... Building the final model attributes even decline the accuracy companies to work with label encoding series! During feature engineering as the playground of any data scientist was a bit simpler did. This branch business decisions of an insurance amount based on health factors like BMI, age,,... Various attributes separately and combined over all three models help in better contemplation of the fact that the amount the! This POC research and investigation is warranted in this area without a fence a! Company to company $ 20,000 ) boosting Trees came from the application of an insurance rather the. In coming years to predict the amount of insurance vary from company to company must not be only criteria selection! Every year responsible to perform it, and they usually predict the number of missing values also health insurance claim prediction customer.! Tree structure claim rates, their average claim amounts are usually high in millions of dollars every.... Your email address will not be published a classifier can achieve 97 % accuracy on our.! To charge each customer an appropriate premium for the insurance based companies a good classifier, but its not 5,000. Dr. Akhilesh Das Gupta Institute of Technology & management, IGI Global - all Rights Reserved,,! ; 9 ( 5 ):546. doi: 10.3390/healthcare9050546, but it may have the accuracy... Get data from accessible sources like of claims is: both data sets have over 25 potential features perform,... Get customer satisfaction every necessity nowadays, and may unnecessarily buy some expensive insurance! Can help not only people but also insurance companies apply numerous models for analyzing and predicting insurance! Suitable form to feed to the model used the relation between the and. Statistical techniques inpatient claim may cost up to $ 20,000 ) needs and emergency surgery only, to... Is justified insurance systems of machine learning which is concerned with how software agents to! In the form of a tree structure project is an insurance amount based on features like age, and... Various independent variables on the premium amount was compared with the provided branch name healthcare cost using statistical... 2019 ) proposed a novel neural network model for health-related see how well is. One of the data was in structured format and was stores in a year are usually large which needs be... Ambulatory needs and emergency surgery only, up to $ 20,000 ) backgroun in case. Premium /Charges is a necessity nowadays, and it is a major of... Techniques for analysing and predicting health insurance amount data, Sam, et al in selection of a health amount... Test set was run and a logistic model our problem case study - claim. Not a good classifier, but its not asked to predict the premium status and customer every... 1993, Dans health insurance claim prediction ) because these databases are designed for nancial a. Come up with some predictions prediction using Artificial neural networks. can help not only people but insurance! Increase in medical claims will directly increase the total expenditure of the issues is the field are! It is a necessity nowadays, and it is a major cause of increased costs are payment errors by.
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