Review of Mental Health Applications using Data Science with ML Techniques
Keywords:
Random Forest, SVM, K-NN, ID3, Naïve Bayes, C4.5, LDTM, Data Analytical FrameworkAbstract
According to the World Health Organization (WHO), amongst 1.3 billion of the country’s population, about 90 million Indians i.e., 7.5 % of them endure one or the other kind of mental disorder. The WHO also predicted that, the population suffering from mental illness would be around 20% by 2020 without foreseeing the corona virus pandemic. That counts over 200 million Indians who may be affected mentally and the number would even more raise and worsen the instances caused due to the other effects of the pandemic such as the caged feeling due to lockdown, loneliness, financial distress, etc. This paper aims at considering few Machine Learning algorithms such as Random Forest, SVM, K-NN, ID3, Naïve Bayes and C4.5 and find the best suitable algorithm for detecting and predicting mental illness accurately. Also, a few SDLC frameworks are considered to provide the integrated results of the algorithms in Mental Health. This survey of algorithms mainly focuses on classifying the emotional states and detecting mental illness in more accurate form. As a result of which, C4.5 algorithm was found to be more accurate.
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