Depression among elderly population is an emerging problem of public health. Various socio demographic factors like age, sex, earning status, living spouse and family type etc are responsible for depression among senior people. Some co morbid conditions like visual problem, hearing difficulties, mobility problem also influence the disease. But depression can be diagnosed at earliest using predi...

Predicting depression can mitigate tragedies. Numerous works have been proposed so far using machine learning algorithms. This paper reviews publications from online electronic databases 2016 to 2020 that use techniques predict depression. The aim of this study is identify important variables used in prediction, recent screening tools adopted, and the latest algorithms used. understanding provi...

Mental health conditions, such as anxiety and depression, are a significant public concern that can have impacts on an individual's quality of life, relationships, overall well-being. In recent years, data science machine learning techniques emerged important tools for early detection mental issues. This research aims at understanding the factors leading to depression implement predictive model...

abstract background: although studies have demonstrated significant negative relationships between quality of life (qol), fatigue, and the most common psychological symptoms (depression, anxiety, stress), the main ambiguity of previous studies on qol is in the relative importance of these predictors. also, there is lack of adequate knowledge about the actual contribution of each of them in the ...

objective: the objective of this study was analyzing the effect of meta-cognition elements on depression, trait and state anxiety symptoms.  methods: in this study, the sample consisted of 224 students of university of social welfare and rehabilitation sciences that answered three questionnaires including metacognitive questionnaire (mcq-30), beck depression inventory (bdi-ii) and spielberger s...