STM Journals Peer-reviewed research from Consortium e-Learning Network

International Journal of Algorithms Design and Analysis Review Review Article

A Study and Prediction of Psychological Disorders Through Machine Learning

  1. Sandeep Mishra Department of Computer Science and Engineering, Dronacharya Group of Institutions, Greater Noida
  2. Garima Sharma Department of Computer Science and Engineering, Dronacharya Group of Institutions, Greater Noida
  3. Chirag Rawat Department of Computer Science and Engineering, Dronacharya Group of Institutions, Greater Noida
  4. Khushi Mishra Department of Computer Science and Engineering, Dronacharya Group of Institutions, Greater Noida
  5. Krishna Aggarwal Department of Computer Science and Engineering, Dronacharya Group of Institutions, Greater Noida
  6. Kaustubh Kumar Shukla Department of Electronics and Communication Engineering, Dronacharya Group of Institutions, Greater Noida

Abstract

Physical illness is very much visible but not psychological illness therefore, it requires more attention and care. Psychological disorders also known as psychiatric disorders refer to a wide range of conditions affecting a person’s thought process, leading to significant changes in the behavior of an individual. The most prevalent psychological disorders include depression, anxiety disorders, and post-traumatic stress disorder (PTSD). Symptoms of psychological disorders vary greatly but include common symptoms like changes in mood and cognition. These disorders undermine an individual ability to function effectively in daily life, impacting their well-being. An individual's behavior and way of thinking are impacted by psychological disorders such as PTSD, anxiety, and depression, which affect their general well-being. They need the right medication, counseling, and assistance. Algorithms for machine learning are essential for both diagnosing and treating these conditions. Through the analysis of vast datasets, the study looks into two widely used supervised learning models, random forest, and decision trees, which can accurately detect and comprehend the origin and course of various conditions. By using these models in healthcare, professionals will be better able to identify these conditions and understand their underlying causes.

Keywords

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