Effectiveness of Mental Health Care by Using Machine Learning on Manufacturing Worker

Abstract

Despite the remarkable advancements in technology that have accelerated and enhanced manufacturing processes, it remains crucial to acknowledge the ongoing significance of variables associated with human mental health. Since the mental health should be cared, the rapid advancement of machine learning techniques has paved the way for innovative applications in the field of mental health care. This paper aims to provide a comprehensive analysis of published studies that utilize machine learning for the diagnosis & detection and treatment & support of mental diseases. The analysis reveals that machine learning techniques have predominantly been used for diagnosing and detecting mental diseases, with a particular emphasis on conditions such as depression, Alzheimer’s disease, and schizophrenia. Moreover, analysis on diagnosis & detection by using machine learning was conducted with a particular focus on categorizing the studies into high and low accuracy based on a threshold of 80%. Moreover, the review highlights the importance of considering various machine learning methods, as well as the utilization of various data types. Nonetheless, the limited number of studies focusing on treatment and support signifies an area that requires further exploration. This review emphasizes the need to minimize variables that may occur in the manufacturing process through mental health care, and mental health care research using machine learning should continue.