Disease Detection Analytics: A Simple Linear Convex Programming Algorithm for Breast Cancer and Diabetes Incidence Decisions

作     者：Mukhopadhyay, Somnath Samaddar, Subhashish Solis, Adriano O. Roy, Asim 

作者机构：Univ Texas El Paso Dept Mkt & Management El Paso TX 79968 USA Georgia State Univ Inst Insight Atlanta GA 30302 USA Georgia State Univ Dept Managerial Sci Atlanta GA 30302 USA York Univ Sch Adm Studies Decis Sci Area Toronto ON M3J 1P3 Canada Arizona State Univ Sch Business Dept Informat Syst Tempe AZ 85281 USA 

出 版 物：《DECISION SCIENCES》 (决策科学)

年 卷 期：2021年第52卷第3期

页      面：661-698页

核心收录：

学科分类：12[管理学] 120202[管理学-企业管理（含：财务管理、市场营销、人力资源管理）] 0202[经济学-应用经济学] 02[经济学] 1202[管理学-工商管理] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 

主　　题：Clinical Data Convex Programming Decision Tree Disease Detection Analytics Linear Programming Neural Network Pattern Classification 

摘      要：In the last couple of decades, data analytics-based pattern classification methods for disease detection have gained much traction in healthcare research and applications. The current study builds linear programming (LP) models for detecting disease incidence. We propose sequential steps of a convex programming algorithm to construct decision boundary functions to classify patterns in disease detection data. We compare the performance of our LP-based classifier with others (neural network, decision tree, k-nearest-neighbor, logistic regression, naive-Bayes, and support-vector-machine) on four datasets: two different ones for breast cancer, and one each for diabetes and diabetic retinopathy. Statistical tests reveal that the LP classifier did significantly better than the other methods in five out of eight false-positive and false-negative test cases. There is not a statistically significant difference in performance in the remaining three tests between the LP classifier and the best alternative method. Most importantly, the LP classifier has significantly superior performance in both diabetes detection and diabetic retinopathy data. The success of the proposed LP classifier results from avoiding modeling noise and memorization of training data. We recommend that our proposed LP classifier be among the set of classifiers for use in disease detection analytics.