Risk assessment for health insurance using equation modeling and machine learning

被引:1
作者
Singh, Amrik [1 ]
Ramkumar, K. R. [1 ]
机构
[1] Chitkara Univ Punjab, Chitkara Univ Inst Engn & Technol, Chandigarh Patiala Natl Highway NH 64, Rajpura 140401, Punjab, India
关键词
Health insurance risk; machine learning; shared dataset; health risk; classification; correlation; regression; CARE; PLATFORM; TECHNOLOGY; PREDICTION; NETWORKS; COVERAGE; DISEASE; SENSOR; CLOUD; WEB;
D O I
10.3233/KES-210065
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Due to the advancement of medical sensor technologies new vectors can be added to the health insurance packages. Such medical sensors can help the health as well as the insurance sector to construct mathematical risk equation models with parameters that can map the real-life risk conditions. In this paper parameter analysis in terms of medical relevancy as well in terms of correlation has been done. Considering it as 'inverse problem' the mathematical relationship has been found and are tested against the ground truth between the risk indicators. The pairwise correlation analysis gives a stable mathematical equation model can be used for health risk analysis. The equation gives coefficient values from which classification regarding health insurance risk can be derived and quantified. The Logistic Regression equation model gives the maximum accuracy (86.32%) among the Ridge Bayesian and Ordinary Least Square algorithms. Machine learning algorithm based risk analysis approach was formulated and the series of experiments show that K-Nearest Neighbor classifier has the highest accuracy of 93.21% to do risk classification.
引用
收藏
页码:201 / 225
页数:25
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