Arkaprabha Sau1, Ishita Bhakta2. 1. Junior Resident, Department of Community Medicine, R.G. Kar Medical College and Hospital, Kolkata, West Bengal, India. 2. PhD Research Fellow, Department of Computer Science and Engineering, West Bengal University of Technology, Kolkata, West Bengal, India.
Abstract
INTRODUCTION: Depression is one of the most important causes of mortality and morbidity among the geriatric population. Although, the aging brain is more vulnerable to depression, it cannot be considered as physiological and an inevitable part of ageing. Various sociodemographic and morbidity factors are responsible for the depression among them. Using Artificial Neural Network (ANN) model depression can be predicted from various sociodemographic variables and co morbid conditions even at community level by the grass root level health care workers. AIM: To predict depression among geriatric population from sociodemographic and morbidity attributes using ANN. MATERIALS AND METHODS: An observational descriptive study with cross-sectional design was carried out at a slum under the service area of Bagbazar Urban Health and Training Centre (UHTC) in Kolkata. Among 126 elderlies under Bagbazar UHTC, 105 were interviewed using predesigned and pretested schedule. Depression status was assessed using 30 item Geriatric Depression Scale. WEKA 3.8.0 was used to develop the ANN model and test its performance. RESULTS: Prevalence of depression among the study population was 45.7%. Various sociodemographic variables like age, gender, literacy, living spouse, working status, personal income, family type, substance abuse and co morbid conditions like visual problem, mobility problem, hearing problem and sleeping problem were taken into consideration to develop the model. Prediction accuracy of this ANN model was 97.2%. CONCLUSION: Depression among geriatric population can be predicted accurately using ANN model from sociodemographic and morbidity attributes.
INTRODUCTION:Depression is one of the most important causes of mortality and morbidity among the geriatric population. Although, the aging brain is more vulnerable to depression, it cannot be considered as physiological and an inevitable part of ageing. Various sociodemographic and morbidity factors are responsible for the depression among them. Using Artificial Neural Network (ANN) model depression can be predicted from various sociodemographic variables and co morbid conditions even at community level by the grass root level health care workers. AIM: To predict depression among geriatric population from sociodemographic and morbidity attributes using ANN. MATERIALS AND METHODS: An observational descriptive study with cross-sectional design was carried out at a slum under the service area of Bagbazar Urban Health and Training Centre (UHTC) in Kolkata. Among 126 elderlies under Bagbazar UHTC, 105 were interviewed using predesigned and pretested schedule. Depression status was assessed using 30 item Geriatric Depression Scale. WEKA 3.8.0 was used to develop the ANN model and test its performance. RESULTS: Prevalence of depression among the study population was 45.7%. Various sociodemographic variables like age, gender, literacy, living spouse, working status, personal income, family type, substance abuse and co morbid conditions like visual problem, mobility problem, hearing problem and sleeping problem were taken into consideration to develop the model. Prediction accuracy of this ANN model was 97.2%. CONCLUSION:Depression among geriatric population can be predicted accurately using ANN model from sociodemographic and morbidity attributes.
Authors: Salvador Tortajada; Juan M García-Gomez; Javier Vicente; Julio Sanjuán; Rosa de Frutos; Rocío Martín-Santos; Luisa García-Esteve; Isolde Gornemann; Alfonso Gutiérrez-Zotes; Francesca Canellas; Angel Carracedo; Monica Gratacos; Roser Guillamat; Enrique Baca-García; Montserrat Robles Journal: Methods Inf Med Date: 2009-03-31 Impact factor: 2.176
Authors: Sarah Graham; Colin Depp; Ellen E Lee; Camille Nebeker; Xin Tu; Ho-Cheol Kim; Dilip V Jeste Journal: Curr Psychiatry Rep Date: 2019-11-07 Impact factor: 5.285
Authors: Gayatri Marathe; Erica E M Moodie; Marie-Josée Brouillette; Joseph Cox; Curtis Cooper; Charlotte Lanièce Delaunay; Brian Conway; Mark Hull; Valérie Martel-Laferrière; Marie-Louise Vachon; Sharon Walmsley; Alexander Wong; Marina B Klein Journal: BMC Med Res Methodol Date: 2022-08-12 Impact factor: 4.612