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Modern people suffer from a variety of behavioral and mental diseases as a result of the added stress and pressure in their daily lives. Just a few examples of mental health problems include schizophrenia, bipolar disorder, anxiety, depression, and stress. Physical and emotional symptoms can both be present in mental illness. This examination will determine whether a person has a mental disorder based on their behaviors and thoughts. Symptoms of mental illness include delusions and hallucinations, as well as panic attacks, sweating, palpitations, melancholy, worry, and excessive thinking. Each symptom denotes a certain type of mental disorder. For this study, only a few machine-learning techniques were used, including decision trees, logistic regression, SVM, and KNN. In this work, we used a feature selection strategy that included a second tree classifier with other pre-processing methods. Based on a patient's symptoms and the feature extraction technique, mental health stability has been identified using a machine-learning algorithm. The
effectiveness of machine learning models was evaluated using Recall, Accuracy, Precision, and F1-score.
"Machine Learning in Mental Health", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.8, Issue 5, page no.826 - 830, May-2023, Available :http://www.ijrti.org/papers/IJRTI2305131.pdf
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator