title: Application of L1/2 regularization logistic method in heart disease diagnosis.
abstract: Heart disease has become the number one killer of human health, and its diagnosis depends on many features, such as age, blood pressure, heart rate and other dozens of physiological indicators. Although there are so many risk factors, doctors usually diagnose the disease depending on their intuition and experience, which requires a lot of knowledge and experience for correct determination. To find the hidden medical information in the existing clinical data is a noticeable and powerful approach in the study of heart disease diagnosis. In this paper, sparse logistic regression method is introduced to detect the key risk factors using L(1/2) regularization on the real heart disease data. Experimental results show that the sparse logistic L(1/2) regularization method achieves fewer but informative key features than Lasso, SCAD, MCP and Elastic net regularization approaches. Simultaneously, the proposed method can cut down the computational complexity, save cost and time to undergo medical tests and checkups, reduce the number of attributes needed to be taken from patients.
authors.affiliation: Faculty of Information Technology & State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Taipa 999078, Macau, China.
authors.forename: Bowen
authors.initials: B
authors.lastname: Zhang
authors.affiliation: Faculty of Information Technology & State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Taipa 999078, Macau, China.
authors.forename: Hua
authors.initials: H
authors.lastname: Chai
authors.affiliation: Faculty of Information Technology & State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Taipa 999078, Macau, China.
authors.forename: Ziyi
authors.initials: Z
authors.lastname: Yang
authors.affiliation: Faculty of Information Technology & State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Taipa 999078, Macau, China.
authors.forename: Yong
authors.initials: Y
authors.lastname: Liang
authors.affiliation: Faculty of Information Technology & State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Taipa 999078, Macau, China.
authors.forename: Gejin
authors.initials: G
authors.lastname: Chu
authors.affiliation: Faculty of Information Technology & State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Taipa 999078, Macau, China.
authors.forename: Xiaoying
authors.initials: X
authors.lastname: Liu
mesh_terms: D000465:Algorithms; D003627:Data Interpretation, Statistical; D020000:Decision Support Systems, Clinical / Q000458:organization & administration*; D003661:Decision Support Techniques; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006331:Heart Diseases / Q000175:diagnosis*; D006801:Humans; D016015:Logistic Models; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D018570:Risk Assessment / Q000379:methods*; D012680:Sensitivity and Specificity
keywords: Heart disease; L1/2 regularization; feature selection; sparse logistic regression