A Computational Intelligence Approach for Predicting Medical Insurance Cost

In the domains of computational and applied mathematics, soft computing, fuzzy logic, and machine learning (ML) are well-known research areas. ML is one of the computational intelligence aspects that may address diverse difficulties in a wide range of applications and systems when it comes to exploi...

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Bibliographic Details
Published in:Mathematical problems in engineering 2021-12, Vol.2021, p.1-13
Main Authors: ul Hassan, Ch. Anwar, Iqbal, Jawaid, Hussain, Saddam, AlSalman, Hussain, Mosleh, Mogeeb A. A., Sajid Ullah, Syed
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Applications of mathematics
Artificial intelligence
Body mass index
Cost estimates
Data mining
Datasets
Decision making
Decision trees
Fraud
Fuzzy logic
Health care
Health care industry
Health insurance
Insurance
Insurance industry
Insurance policies
Machine learning
Mathematical problems
Medical research
Model accuracy
Regression analysis
Regression models
Soft computing
Support vector machines
Underwriting
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Summary:In the domains of computational and applied mathematics, soft computing, fuzzy logic, and machine learning (ML) are well-known research areas. ML is one of the computational intelligence aspects that may address diverse difficulties in a wide range of applications and systems when it comes to exploitation of historical data. Predicting medical insurance costs using ML approaches is still a problem in the healthcare industry that requires investigation and improvement. Using a series of machine learning algorithms, this study provides a computational intelligence approach for predicting healthcare insurance costs. The proposed research approach uses Linear Regression, Support Vector Regression, Ridge Regressor, Stochastic Gradient Boosting, XGBoost, Decision Tree, Random Forest Regressor, Multiple Linear Regression, and k-Nearest Neighbors A medical insurance cost dataset is acquired from the KAGGLE repository for this purpose, and machine learning methods are used to show how different regression models can forecast insurance costs and to compare the models’ accuracy. The results shows that the Stochastic Gradient Boosting (SGB) model outperforms the others with a cross-validation value of 0.0.858 and RMSE value of 0.340 and gives 86% accuracy.
ISSN:1024-123X
1563-5147
DOI:10.1155/2021/1162553