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首页> 外文期刊>Journal of Clinical and Diagnostic Research >Artificial Neural Network (ANN) Model to Predict Depression among Geriatric Population at a Slum in Kolkata, India VC01-VC04
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Artificial Neural Network (ANN) Model to Predict Depression among Geriatric Population at a Slum in Kolkata, India VC01-VC04

机译：人工神经网络（ANN）模型可预测印度加尔各答贫民区老年人口的抑郁状况VC01-VC04

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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.
机译：简介：抑郁症是老年人群中最重要的死亡和发病原因之一。尽管衰老的大脑更容易遭受抑郁症的困扰，但不能将其视为生理现象和衰老的必然部分。各种社会人口统计学和发病因素是造成抑郁症的原因。使用人工神经网络（ANN）模型，即使在社区一级，基层卫生保健工作者也可以从各种社会人口统计学变量和共病条件预测抑郁症。目标：使用ANN从社会人口统计学和发病率属性预测老年人群中的抑郁症。方法：在加尔各答的巴格巴扎尔城市卫生和培训中心（UHTC）服务区下的贫民窟进行了横截面设计的观察性描述性研究。在Bagbazar UHTC领导下的126名老年人中，有105名接受了预先设计和预先测试的时间表的采访。使用30项老年抑郁量表评估抑郁状态。结果：WEKA 3.8.0用来开发ANN模型并测试其性能。结果：研究人群中抑郁症的患病率为45.7％。考虑到各种社会人口统计学变量，如年龄，性别，识字率，配偶的生活状况，工作收入，个人收入，家庭类型，药物滥用以及诸如视觉问题，行动问题，听力问题和睡眠问题之类的合并病，以开发该模型。该人工神经网络模型的预测准确率为97.2％。结论：利用人工神经网络模型可以从社会人口统计学和发病率属性准确预测老年人群中的抑郁症。

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