Predictive Apriori Algorithm in Youth Suicide Prevention by Screening Depressive Symptoms from Patient Health Questionnaire-9
Predictive Apriori Algorithm in Youth Suicide Prevention by Screening Depressive Symptoms from Patient Health Questionnaire-9
Author(s): Yaowarat Sirisathitkul, Putthiporn Thanathamathee, Saifon AekwarangkoonSubject(s): ICT Information and Communications Technologies
Published by: UIKTEN - Association for Information Communication Technology Education and Science
Keywords: Depression; Feature selection; Predictive Apriori algorithm; Random forest; Suicidal risk
Summary/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.
Journal: TEM Journal
- Issue Year: 8/2019
- Issue No: 4
- Page Range: 1449-1455
- Page Count: 7
- Language: English
