Predicting suicidal ideation among Filipino youth an application of modeling with class imbalance data - Bonitez, Teodolfo F.
Abstract (Class imbalance data is a common for many field of researches, especially when the variable of interest measures a rare event, characteristics, or instance. Class imbalance occurs when the ratio of instances between the larger class (or majority group) and smaller class (or minority group) is very large. In this study, models for predicting suicidal ideation among Filipino youth are constructed. To address potential class imbalance data issues, this study utilizes data-level and algorithm-level approaches. Data-level approach consists of resampling methods (oversampling, undersampling, and hybrid techniques), while algorithm-level approaches considered in this study are Support Vector Machine (SVM), Fuzzy SVM, Adaptive Boosting, and Logistics Regression. The data is derived from the 2015 National Youth Assessment Study. Results suggest that relatively higher/improved sensitivity measure are observed using logistic regression and SVM models tend to have high measure in correctly classifying the minority group. From the different models implemented in the actual survey data, several predictors of suicidal ideation are identified: youth who do farm to one's self, youth who have negative perception in life, youth with anorexic symptom, difference between urban and rural living, and average monthly income).