Citation

Sirisathitkul Y, Thanathamathee P, Aekwarangkoon S. TEM Journal 2019; 8(4): 1449-1455.

Copyright

(Copyright © 2019)

DOI

10.18421/TEM84-49

PMID

unavailable

Abstract

This study employed the Predictive A priori algorithm in identifying significant questions of Patient Health Questionnaire-9 (PHQ-9) for suicide tendency prediction by using PHQ-9 and suicidal screening form (8Q). The random forest was applied to calculate the classification accuracy of PHQ-9 and 3 feature selection algorithms were applied to determine the attribute importance. The Predictive Apriori algorithm was applied to find the meaningful association rules. The classification accuracy of PHQ-9 is 92.12% and item no. 1 and no. 9 of PHQ-9 are less important. The significant risk factors associated with suicidal ideation are Item no. 2, no. 4, and no. 5. © 2019 Yaowarat Sirisathitkul, Putthiporn Thanathamathee, Saifon Aekwarangkoon.

Language: en

Keywords

Depression; Suicidal risk; Feature selection; Predictive apriori algorithm; Random forest