Analysis of Post-Flood Mental Illness Using Machine Learning Techniques: A Review

Every year, floods in India damage infrastructure, property, and livelihoods for millions of people. The emotional health of flood victims is as essential as the physical and financial consequences. This article summarizes prior research on mental health issues after natural catastrophes, particularly floods, in India. The review discusses data availability, interpretability, and ethical issues when using machine learning techniques for psychological disorder analysis, highlights these mental health issues in post-flood recovery efforts, and suggests future interventions. It also stresses the importance of ethical behavior and data security when applying machine learning algorithms to mental health. Finally, the review summarizes the research and its consequences, emphasizing machine learning's potential to improve post-flood mental health understanding. Based on the available data, it is evident that further research is required in order to draw any meaningful conclusions. The user's text proposes potential avenues for enhancing machine learning-based methodologies, including the development of more interpretable models and the integration of diverse datasets.