Application of classification techniques on development an early-warning system for chronic illnesses

Authors:
Chih-Hung Jen;Chien-Chih Wang;Bernard C. Jiang;Yan-Hua Chu;Ming-Shu Chen
Affiliations:
Department of Information Management, Lunghwa University of Science and Technology, Taiwan;Department of Industrial Engineering and Management, Ming Chi University of Technology, Taiwan;Department of Industrial Engineering and Management, Yuan Ze University, Taiwan;Department of Industrial Engineering and Management, Ming Chi University of Technology, Taiwan;Department of Health Management Center, Far Eastern Memorial Hospital, Taiwan
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

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Abstract

Chronic disease has gradually become a major fatality cause in Taiwan. Being afflicted with such illnesses elevate vulnerability to other complications as well. Therefore, this paper adopts a preventative perspective and ascertains the impacts of important physiological indicators and clinical test values for various chronic illnesses. The paper investigates five chronic diseases: hypertension, diabetes, cardiovascular disease, disease of the liver, and renal disease. Utilizing chronic diseases risk factors to establish early-warning criteria may reduce the complication occurrence rate. K-nearest neighbor, linear discriminant analysis, and sequential forward selection are utilized, which is divided into two parts. The first part classifies and screens both healthy persons and those affiliated with the abovementioned chronic illnesses for characteristic value determination. The second part determines the critical value of the important risk factors of each chronic illness and builds early-warning criteria to recognition the chronic illnesses. This paper uses data from a medical center in Taiwan to verify the proposed methodology. The results reveal that classifying materials and screening important factors are both positively efficient with a corrected rate of 80%. Additionally, through the important factors of early-warning criteria, not only can help patients understand the risks of suffering diseases, but also effectively offer diagnosis reference criteria for medical personnel.