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)? The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. 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. 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. Reinforcement learning is getting very common in nowadays, therefore this field is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulated-based optimization, multi-agent systems, swarm intelligence, statistics and genetic algorithms. It was observed that a persons age and smoking status affects the prediction most in every algorithm applied. Creativity and domain expertise come into play in this area. In addition, only 0.5% of records in ambulatory and 0.1% records in surgery had 2 claims. Health-Insurance-claim-prediction-using-Linear-Regression, SLR - Case Study - Insurance Claim - [v1.6 - 13052020].ipynb. 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. 11.5 second run - successful. That predicts business claims are 50%, and users will also get customer satisfaction. arrow_right_alt. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. In the past, research by Mahmoud 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. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. 2 shows various machine learning types along with their properties. Once training data is in a suitable form to feed to the model, the training and testing phase of the model can proceed. Health Insurance Claim Prediction Using Artificial Neural Networks. 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. A tag already exists with the provided branch name. (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. The authors Motlagh et al. It helps in spotting patterns, detecting anomalies or outliers and discovering patterns. Claim rate is 5%, meaning 5,000 claims. Logs. And, just as important, to the results and conclusions we got from this POC. Taking a look at the distribution of claims per record: This train set is larger: 685,818 records. Going back to my original point getting good classification metric values is not enough in our case! Random Forest Model gave an R^2 score value of 0.83. From the box-plots we could tell that both variables had a skewed distribution. Take for example the, feature. Usually a random part of data is selected from the complete dataset known as training data, or in other words a set of training examples. Insurance companies are extremely interested in the prediction of the future. Keywords Regression, Premium, Machine Learning. The model predicted the accuracy of model by using different algorithms, different features and different train test split size. \Codespeedy\Medical-Insurance-Prediction-master\insurance.csv') data.head() Step 2: 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 . Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. This Notebook has been released under the Apache 2.0 open source license. "Health Insurance Claim Prediction Using Artificial Neural Networks.". This is the field you are asked to predict in the test set. Continue exploring. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. Actuaries are the ones who are responsible to perform it, and they usually predict the number of claims of each product individually. 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. 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. In the next blog well explain how we were able to achieve this goal. Using the final model, the test set was run and a prediction set obtained. Last modified January 29, 2019, Your email address will not be published. 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. 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. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. The Company offers a building insurance that protects against damages caused by fire or vandalism. Insurance companies apply numerous techniques for analyzing and predicting health insurance costs. The models can be applied to the data collected in coming years to predict the premium. We already say how a. model can achieve 97% accuracy on our data. 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. Logs. It has been found that Gradient Boosting Regression model which is built upon decision tree is the best performing model. 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. Dong et al. Decision on the numerical target is represented by leaf node. In the next part of this blog well finally get to the modeling process! Understand the reasons behind inpatient claims so that, for qualified claims the approval process can be hastened, increasing customer satisfaction. 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. These claim amounts are usually high in millions of dollars every year. Coders Packet . 4 shows the graphs of every single attribute taken as input to the gradient boosting regression model. The most prominent predictors in the tree-based models were identified, including diabetes mellitus, age, gout, and medications such as sulfonamides and angiotensins. 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. 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. Factors determining the amount of insurance vary from company to company. I like to think of feature engineering as the playground of any data scientist. Although every problem behaves differently, we can conclude that Gradient Boost performs exceptionally well for most classification problems. This feature equals 1 if the insured smokes, 0 if she doesnt and 999 if we dont know. Your email address will not be published. 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! i.e. 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. According to Kitchens (2009), further research and investigation is warranted in this area. 1 input and 0 output. Settlement: Area where the building is located. Description. You signed in with another tab or window. can Streamline Data Operations and enable In I. The size of the data used for training of data has a huge impact on the accuracy of data. However since ensemble methods are not sensitive to outliers, the outliers were ignored for this project. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Users will also get information on the claim's status and claim loss according to their insuranMachine Learning Dashboardce type. 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. Some of the work investigated the predictive modeling of healthcare cost using several statistical techniques. In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. II. 99.5% in gradient boosting decision tree regression. of a health insurance. However, it is. Where a person can ensure that the amount he/she is going to opt is justified. In this paper, a method was developed, using large-scale health insurance claims data, to predict the number of hospitalization days in a population. Each plan has its own predefined . history Version 2 of 2. 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. Attributes are as follow age, gender, bmi, children, smoker and charges as shown in Fig. Also people in rural areas are unaware of the fact that the government of India provide free health insurance to those below poverty line. 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. Fig 3 shows the accuracy percentage of various attributes separately and combined over all three models. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. Required fields are marked *. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. A comparison in performance will be provided and the best model will be selected for building the final model. There are many techniques to handle imbalanced data sets. (2011) and El-said et al. Application and deployment of insurance risk models . 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. On the other hand, the maximum number of claims per year is bound by 2 so we dont want to predict more than that and no regression model can give us such a grantee. Gradient boosting involves three elements: An additive model to add weak learners to minimize the loss function. Users can quickly get the status of all the information about claims and satisfaction. In our case, we chose to work with label encoding based on the resulting variables from feature importance analysis which were more realistic. 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). Already say how a. model can achieve 97 % accuracy on our data business, two things considered. Any data scientist to outliers, the training and testing phase of the repository investigated predictive... The approval process can be applied to the results and conclusions we got this. To think of feature engineering as the playground of any data scientist claims per:. Information about claims and satisfaction more health centric insurance amount already say how a. model can 97... 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