Implementation Data Mining using Decision Tree Method-Algorithm C4.5 for Postpartum Depression Diagnosis

Abstract

Postpartum depression is a serious problem that needs to be addressed because it has negative effects on family, child welfare, cognitive, and mother child interactions. Diagnosis is done based on psychological condition, blood pressure, respiration, body temperature, and classification data extract by decision tree C4.5 algorithm method. Results of this study in the form of an online information system that can identify the level of depression more quickly and precisely. The results showed the greatest gain on the psychological variables of 0.57 node 1, blood pressure 0.54 node 2, body temperature 0,54 node 3, means that the three variables are more influential on the condition of depressed patients, and should be given priority treatment. Test results from 50 patients with 50 examinations showed 62% prevalence, 65.62% sensitivity, specificity 77.77%, negative predictive value of 56%, and positive predictive value 84%.