Paper Title
A Comprehensive Analysis on Smart Discovery of Parents’ Mental Health of Disabled Children

Abstract
This research work provides a thorough examination of statistics information about the mental well-being of parents of disabled children. We seek to present a comprehensive picture of the difficulties these parents encounter by utilizing a variety of sources, including government datasets, national surveys, and peer-reviewed research. The entire analysis of earlier studies as well as a fresh hypothesis are presented in this study to predict the mental health of these parents. Existing research relied on regression and descriptive analysis, we employ a diverse array of methodologies including descriptive analysis, meta-analysis, logistic regression and proposed method is based on classification methods to predict and comprehend parental mental health of disabled children. Our primary objective is to predict parents’ mental well-being based on their children’s disabilities and the influential factors affecting parental welfare. This research exploring correlations between parents and disabled children, while scrutinizing key determinants such as healthcare services, family history, workplace environments, types of disabilities, gender, age, and more. Leveraging classification techniques, our study seeks to classify individuals based on their likelihood of experiencing mental health problem. This research work is based the classification method like SVM, KNN, logistic regression, XGboost, decision tree to predict the mental of parents and give the best accuracy result. Finally, we provide a comparative review of previous research on parents' mental health along with solution suggestions based on categorization methods, thereby advancing intelligent exploration in this area. Keywords - Machine Learning, Parents’ Mental Health, Disability, Statistical Analysis, Disabled Children