Abstract
Psychiatry focuses on one of the greatest issues of public health. There are numerous indicators that can be utilized to assess the mental capacity of a person’s mental health. Several factors can affect the physical and financial health of a person. Psychiatrist treatment can result in mental illness. Schizophrenia primarily affects women and can be lethal. Men are more likely than women to exhibit symptoms of this illness. Antisocial conduct is caused by mental illness, which distorts social interactions. Consequently, social issues that were already obvious have spread. Adults in their 20 s and 30 s are susceptible to anxiety, substance misuse, hazardous conduct, hubris, suicidal thoughts, despair, bewilderment, and consciousness, according to a global survey. Mental diseases have been increased from 10.5% in 1990 to 19.86% in 2022 and psychiatry is account for 14.3% of total death worldwide (all about eight million). A labor-intensive survey questionnaire was utilized to obtain data. Combining surveys for distinct psychiatric conditions essential components from various research studies yielded the optimal response to the numerical value translation. After data collection, applying normalization technique psychiatric features are extracted from a processed dataset. We projected a machine learning classifier to categorize extracted features by using K-Nearest Neighbor (KNN), Polynomial Kernel SVM, Naïve Bayes, Decision tree, and Logistic regression classifiers on a scaled dataset of mental patients. This study proposes polynomial kernel SVM as a classifier to predict the psychiatric disease of each patient by comparing its performance to that of all other classifiers.
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Rahman, M.S., Ahmed, B. (2023). A Comparative Study of Psychiatric Characteristics Classification for Predicting Psychiatric Disorder. In: Kaiser, M.S., Waheed, S., Bandyopadhyay, A., Mahmud, M., Ray, K. (eds) Proceedings of the Fourth International Conference on Trends in Computational and Cognitive Engineering. Lecture Notes in Networks and Systems, vol 618. Springer, Singapore. https://doi.org/10.1007/978-981-19-9483-8_16
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DOI: https://doi.org/10.1007/978-981-19-9483-8_16
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