An Efficient Early Diagnosis and Healthcare Monitoring System for Mental Disorders Using Machine Learning

Abstract

Recently, the increase in internal health problems in society has led to an increase in research on the development of mechanistic capacity models to detect or predict internal mental health. The effective use of internal health assessments or discovery models allows internal health interpreters to redefine internal suffering more objectively than ever before, and in the early stages when interventions may be more effective. In this chapter, the authors aim to apply a bias mitigation system based on multitasking literacy to perform a fairness analysis and to fear the predicted model using the Reddit dataset. This chapter employs an efficient technique for machine learning random forests. The proposed model was evaluated against various performance metrics and the model showed 91.00% accuracy. This is an advantage compared to existing approaches.