Soo Kyoung Lee|Bo?Yeong Kang|Hong-Gee Kim|Youn?Jung Son
Predictors of Medication Adherence in Elderly Patients with Chronic Diseases Using Support Vector Machine Models
Seoul National University|Kyungpook National University|Seoul National University|Soonchunhyang University
aim study establish prediction model medication adherence elderly patients chronic diseases identify variables showing highest classification accuracy medication adherence elderly patients chronic diseases using support vector machine svm conventional statistical methods logistic regression lrwe included 293 chronic disease patients older 65 years treated one tertiary hospital medication adherence moriskys selfreport used data collected facetoface interviews mean age patients 738 years classification process performed lr spss ver 200 svm matlab ver 712 methodtaking account 16 variables predictors result applying lr svm classification accuracy 711 973 respectively listed top nine variables selected svm accuracy using single variable selfefficacy 724 results suggest selfefficacy key factor medication adherence among korean elderly population lr svmmedication nonadherence strongly associated selfefficacy also modifiable factors depression health literacy medication knowledge associated medication nonadherence identified since svm builds optimal classifier minimize empirical classification errors discriminating patient samples could achieve higher accuracy smaller number variables number variables used lr applications approach areas complex diseases treatment provide uncharted potentials researchers domains