Predicting Psychiatric Disorder from the Classified Psychiatric Characteristics using Machine Learning Algorithm

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  •   Md Sydur Rahman

  •   Boshir Ahmed

Abstract


Psychiatry is an interdisciplinary field that focuses on a major public health concern. The following threats represent a significant risk to the mental health of anyone who is exposed to them. Various things might have an impact on both a person's physical and financial health. Psychiatric treatment can raise the likelihood of getting a mental disorder. Schizophrenia is a devastating mental condition that predominantly affects women. It is more prevalent in both male and female. Mentally sick individuals are more likely to engage in antisocial behavior which results in social behavioral distortion. As a direct result, societal concerns that were already evident have grown considerably more prevalent. According to global data, anxiety, drugs misuse, hazardous behavior, arrogance, suicidal thoughts, depression, disorientation, and consciousness are widespread among 20 to 30 years adults. These individuals are continuously searching for something new, which can be detrimental to their mental health because it makes them less stable. 




A machine learning classifier has been predicted to classify the appropriate extracted features after applying Support Vector Machine (SVM) classifiers for various no-linear kernels, taking into account all the critical elements for psychiatry that have been stated. An unequal history of mental diseases has climbed from 10.5 percent in 1990 to 19.86 percent in 2022, according to Adult Prevalence of Mental Illness (AMI) 2022, and psychiatry currently accounts for 14.3 percent of yearly global mortality (approximately 8 million).



Keywords: Performance Analysis & Prediction, psychiatry, result comparison, SVM

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How to Cite
Rahman, M. S., & Ahmed, B. (2022). Predicting Psychiatric Disorder from the Classified Psychiatric Characteristics using Machine Learning Algorithm. European Journal of Information Technologies and Computer Science, 2(4), 5–10. https://doi.org/10.24018/compute.2022.2.4.72