期刊
INTERNATIONAL JOURNAL OF INTERACTIVE MULTIMEDIA AND ARTIFICIAL INTELLIGENCE
卷 5, 期 3, 页码 61-71出版社
UNIV INT RIOJA-UNIR
DOI: 10.9781/ijimai.2018.10.001
关键词
XGBoost; Meta-Cognitive Neural Network; Projection-based Learning; Particle Swann Optimization; Depression; MYNAH Cohort
资金
- Wellcome DBT India Alliance
- Medical Research Council, UK
- Department for International Development, UK
Depression is a burdensome psychiatric disease common in low and middle income countries causing disability, morbidity and mortality in late life. 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 is developed using machine learning approach for classification of depression using Meta-Cognitive Neural Network (McNN) classifier with Projection-based learning (PBL) to address the self-regulating principles like how; what and Alen to learn. XGBoost is used for feature selection on the available data of assessments with improved confidence. To improve the efficiency of McNN-PBL classifier the best parameters are found using Particle Swarm Optimization (PSO) algorithm. The results indicate that the McNN-PBL classifier selects appropriate records to learn and remove repetitive records which improve the generalization performance. The study helps the clinician to identify the best parameters to analyze the patient.
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