Exploratory Boosted Feature Selection and Neural Network Framework for Depression Classification

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 when 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 McNNPBL 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

Exploratory Boosted Feature Selection and Neural Network Framework for Depression Classification

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 when 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 McNNPBL 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

Validation of EURO-D, a geriatric depression scale in South India: Findings from the Mysore Study of Natal effects on Ageing and Health (MYNAH).

Introduction: Many of the assessment tools used to study depression among older people in low- and middle- income countries (LMICs) are adaptations of instruments developed in other cultural settings. There is a need to validate those instruments in LMICs.Methods: 721 men and women aged 55-80 years from the Mysore Birth Records Cohort underwent standardised assessments for sociodemographic characteristics, cardiometabolic risk factors, cognitive function and mental health. Sensitivity, specificity and level of agreement of EURO-D diagnosis of depression with diagnosis of depression derived by the Geriatric Mental State (GMS) examination were calculated. To validate the EURO-D score against GMS depressive episode, we used maximum Youden's index as the criterion for each cut-off point. Concurrent validity was assessed by measuring correlations with the WHO Disability Assessment Schedule (WHO DAS II).Results: Of the 721 (408 men and 313 women) who participated in this study, 138 (54 men and 84 women) were diagnosed with depression. Women had higher depression scores on the EURO-D scale and disability on the WHO DAS II scale. A maximum Youden's Index of 0.60 was observed at a EURO-D cut-off of 6, which corresponded to 95% sensitivity, 64% specificity, kappa value of 0.6 and area under the curve (AUC) of 80%. There was significant and positive correlation between EURO-D and WHO DAS II scores.Limitations: Future independent validation studies in other settings are required.Discussion: This study supports the use of the EURO-D scale for diagnosing depression among older adults in South India.KeywordsValidationEURO-DGeriatric DepressionIndi