Smooth support vector machine for suicide-related behaviours prediction

Abstract

Suicide-related behaviours need to be prevented on psychiatric patients. Prediction of those behaviours based on patient medical records would be very useful for the prevention by the psychiatric hospital. This research focused on developing this prediction at the only one psychiatric hospital of Bali Province by using Smooth Support Vector Machine method, as the further development of Support Vector Machine. The method used 30.660 patient medical records from the last five years. Data cleaning gave 2665 relevant data for this research, includes 111 patients that have suicide-related behaviours and under active treatment. Those cleaned data then were transformed into ten predictor variables and a response variable. Splitting training and testing data on those transformed data were done for building and accuracy evaluation of the method model. Based on the experiment, the best average accuracy at 63% can be obtained by using 30% of relevant data as data testing and by using training data which has one-to-one ratio in number between patients that have suicide-related behaviours and patients that have no such behaviours. In the future work, accuracy improvement need to be confirmed by using Reduced Support Vector Machine method, as the further development of Smooth Support Vector Machine.