A Boosted Machine Learning Approach For Detection of Depression

Depression is a common psychiatric disorder in older people which is burdensome especially in low and middle income countries. In this study, we demonstrate a novel approach for detection of depression using clinical data obtained from the on-going MYsore studies of Natal effects on Ageing and Health (MYNAH), in south India where the members have undergone a comprehensive assessment for cognitive function, mental health and cardiometabolic disorders. The proposed model developed using XGBoost helps in detection of depression in MYNAH cohort by making use of feature selection on the available data of assessments with improved confidence.