An Efficient Novel Approach for Early Detection of Mental Health Disorders Through Distributed Machine Learning Paradigms from Public Societal Communication
Keywords:
Machine learning, World Health Organization, mental health, societalAbstract
The main factor contributing to impairment worldwide is mental illness. In underdeveloped countries, approximately 2 million people commit suicide annually, and 85% of those who have mental health issues go untreated. According to a study, using social media excessively might cause depression and anxiety. This research study focuses on social media, specifically Facebook, Twitter, and Instagram, and mental health. Using PRISMA criteria on PubMed and Google Scholar, a search of the literature was conducted from January 2010 to June 2022 to find studies addressing the relationship between social media sites and mental health. Social media can provide users with a sense of community, but excessive and rising use of it, especially among the weak, is associated with depression and other mental health issues. The World Health Organization (WHO) estimates that anxiety affects one in every thirteen individuals worldwide. According to the WHO, anxiety disorders are the most common kind of mental illness in the world, with specific phobias, major depressive disorder, and social phobia coming in first and second, respectively. The World Health Organization (WHO) estimates that anxiety affects one in every thirteen individuals worldwide. According to the WHO, anxiety disorders are the most common kind of mental illness in the world, with specific phobias, major depressive disorder, and social phobia coming in first and second, respectively. Depression can be brought on by a variety of events, such as the death of a loved one, the loss of a job, a divorce, and other traumatic situations. These emotions are normal when we are worried. Everyone has experienced sorrow. Contrarily, depression and depression are not the same thing. Depression is a psychological ailment that requires pharmacological treatment. This research blends personal interests in random forest, stacking, and boosting algorithms with mental health. Artificial intelligence's branch of machine learning is frequently used to identify illnesses. It also gives doctors a platform to evaluate vast amounts of patient data and come up with the best course of action based on the patient's medical condition.
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