Depression Risk Model Among Malaysians

Abstract

Depression is a debilitative disease that affects over 300 million people all around the globe. It affects the functionality of people suffering from it, which implicates to socioeconomic burden to individual, families and societal levels. The subjectivity symptoms and signs in diagnosing depression on patients is a great problem among psychiatrists and psychologists. By building a depression risk model, it helps physician to identify depression with higher efficiency, accuracy and specificity. Healthcare will be improved in terms of cutting costs, time of service and energy to serve the patients. By Machine Learning, specifically Supervised Learning uses classifiers and feature extraction tools to identify what are the most significant factors to diagnose depression. This method helps to build a risk model which helps to improve in identifying depression among liable patients.