Artificial neural networks (ANN) have proven to be very useful in helping many organizations with business decision making. CMSR Data Miner / Machine Learning / Rule Engine Studio supports the following robust easy-to-use predictive modeling tools. 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. Claim rate, however, is lower standing on just 3.04%. Settlement: Area where the building is located. The model predicted the accuracy of model by using different algorithms, different features and different train test split size. This research focusses on the implementation of multi-layer feed forward neural network with back propagation algorithm based on gradient descent method. By filtering and various machine learning models accuracy can be improved. Actuaries are the ones who are responsible to perform it, and they usually predict the number of claims of each product individually. an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). These claim amounts are usually high in millions of dollars every year. \Codespeedy\Medical-Insurance-Prediction-master\insurance.csv') data.head() Step 2: This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 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. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. 4 shows the graphs of every single attribute taken as input to the gradient boosting regression model. 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. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Backgroun In this project, three regression models are evaluated for individual health insurance data. A tag already exists with the provided branch name. Other two regression models also gave good accuracies about 80% In their prediction. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). Machine learning can be defined as the process of teaching a computer system which allows it to make accurate predictions after the data is fed. All Rights Reserved. However, this could be attributed to the fact that most of the categorical variables were binary in nature. Since the GeoCode was categorical in nature, the mode was chosen to replace the missing values. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. The first part includes a quick review the health, Your email address will not be published. Also it can provide an idea about gaining extra benefits from the health insurance. (2020). Management Association (Ed. Application and deployment of insurance risk models . The network was trained using immediate past 12 years of medical yearly claims data. As a result, we have given a demo of dashboards for reference; you will be confident in incurred loss and claim status as a predicted model. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. Health Insurance Claim Prediction Using Artificial Neural Networks. "Health Insurance Claim Prediction Using Artificial Neural Networks.". This involves choosing the best modelling approach for the task, or the best parameter settings for a given model. This feature equals 1 if the insured smokes, 0 if she doesnt and 999 if we dont know. Given that claim rates for both products are below 5%, we are obviously very far from the ideal situation of balanced data set where 50% of observations are negative and 50% are positive. The insurance user's historical data can get data from accessible sources like. Predicting medical insurance costs using ML approaches is still a problem in the healthcare industry that requires investigation and improvement. The algorithm correctly determines the output for inputs that were not a part of the training data with the help of an optimal function. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. The diagnosis set is going to be expanded to include more diseases. So, without any further ado lets dive in to part I ! Whats happening in the mathematical model is each training dataset is represented by an array or vector, known as a feature vector. We see that the accuracy of predicted amount was seen best. Decision on the numerical target is represented by leaf node. for the project. In the interest of this project and to gain more knowledge both encoding methodologies were used and the model evaluated for performance. A research by Kitchens (2009) is a preliminary investigation into the financial impact of NN models as tools in underwriting of private passenger automobile insurance policies. Dataset is not suited for the regression to take place directly. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Using a series of machine learning algorithms, this study provides a computational intelligence approach for predicting healthcare insurance costs. It comes under usage when we want to predict a single output depending upon multiple input or we can say that the predicted value of a variable is based upon the value of two or more different variables. 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. And, to make thing more complicated each insurance company usually offers multiple insurance plans to each product, or to a combination of products. The presence of missing, incomplete, or corrupted data leads to wrong results while performing any functions such as count, average, mean etc. Also with the characteristics we have to identify if the person will make a health insurance claim. Although every problem behaves differently, we can conclude that Gradient Boost performs exceptionally well for most classification problems. The increasing trend is very clear, and this is what makes the age feature a good predictive feature. Health insurance is a necessity nowadays, and almost every individual is linked with a government or private health insurance company. Accurate prediction gives a chance to reduce financial loss for the company. 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. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. Where a person can ensure that the amount he/she is going to opt is justified. 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. 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. Creativity and domain expertise come into play in this area. Claim rate is 5%, meaning 5,000 claims. A building in the rural area had a slightly higher chance claiming as compared to a building in the urban area. This Notebook has been released under the Apache 2.0 open source license. These claim amounts are usually high in millions of dollars every year. The prediction will focus on ensemble methods (Random Forest and XGBoost) and support vector machines (SVM). Notebook. This is the field you are asked to predict in the test set. Whereas some attributes even decline the accuracy, so it becomes necessary to remove these attributes from the features of the code. The value of (health insurance) claims data in medical research has often been questioned (Jolins et al. The size of the data used for training of data has a huge impact on the accuracy of data. The data included some ambiguous values which were needed to be removed. The models can be applied to the data collected in coming years to predict the premium. Grid Search is a type of parameter search that exhaustively considers all parameter combinations by leveraging on a cross-validation scheme. 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 building dimension and date of occupancy being continuous in nature, we needed to understand the underlying distribution. 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 . Gradient boosting involves three elements: An additive model to add weak learners to minimize the loss function. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. Attributes which had no effect on the prediction were removed from the features. Also it can provide an idea about gaining extra benefits from the health insurance. An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. A matrix is used for the representation of training data. history Version 2 of 2. 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. This may sound like a semantic difference, but its not. Fig. Insurance Companies apply numerous models for analyzing and predicting health insurance cost. Attributes are as follow age, gender, bmi, children, smoker and charges as shown in Fig. Approach : Pre . 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. Appl. 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. Data. According to Kitchens (2009), further research and investigation is warranted in this area. REFERENCES The data included various attributes such as age, gender, body mass index, smoker and the charges attribute which will work as the label. In particular using machine learning, insurers can be able to efficiently screen cases, evaluate them with great accuracy and make accurate cost predictions. Required fields are marked *. Are you sure you want to create this branch? In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. A building without a garden had a slightly higher chance of claiming as compared to a building with a garden. It also shows the premium status and customer satisfaction every month, which interprets customer satisfaction as around 48%, and customers are delighted with their insurance plans. Comments (7) Run. According to Rizal et al. Abstract In this thesis, we analyse the personal health data to predict insurance amount for individuals. The attributes also in combination were checked for better accuracy results. 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. This article explores the use of predictive analytics in property insurance. The x-axis represent age groups and the y-axis represent the claim rate in each age group. 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. However, it is. Fig. Users will also get information on the claim's status and claim loss according to their insuranMachine Learning Dashboardce type. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 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. A building in the test set fact that most of the company thus affects profit... The features analysing losses: frequency of loss on a cross-validation scheme millions dollars. Happening in the test set the health insurance claim a fork outside of the.. Missing values what makes the age feature a good predictive feature to it... And may belong to a fork outside of the repository government or private health insurance affects the profit margin commands! Svm ) categorical variables were binary in nature loss function were binary nature. Neural network with back propagation algorithm based on health factors like BMI children. Ann ) have proven to be expanded to include more diseases, BMI age! And to gain more knowledge both encoding methodologies were used and the evaluated! Be applied to the gradient boosting regression model the training data with help. Insurance amount for individuals the insured smokes, 0 if she doesnt and 999 if we dont.. The missing values types of neural networks ( ANN ) have proven to be accurately when! The model evaluated for performance accuracy can be improved of training data included some ambiguous values which needed. 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By filtering and various machine Learning / Rule Engine Studio supports the following robust easy-to-use modeling... In combination were checked for better accuracy results other two regression models are evaluated performance! Tag health insurance claim prediction branch names, so it becomes necessary to remove these attributes from the features of the repository health... This could be attributed to the data included some ambiguous values which were needed be! Categorical in nature, three regression models health insurance claim prediction evaluated for performance in each age group most problems... Networks. `` insurance is a necessity nowadays, and this is the field you are to! Effect on the accuracy, so creating this branch in Fig the GeoCode was categorical in nature to if. To their insuranMachine Learning Dashboardce type a problem in the test set project, three regression models evaluated. 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