Parents’ Mental Health Detection Having Children with Disabilities Using Machine Learning Technique

Abstract

The journey of parents raising children with disabilities is fraught with unique challenges, underscoring the critical need for comprehensive research to illuminate statistical insights that can drive informed policy, bolster support systems, and inspire community initiatives. This study leverages a diverse array of sources, including national surveys, government databases, and peer-reviewed studies, to provide a nuanced understanding of the multifaceted experiences of these parents. Through meticulous data analysis spanning various dimensions, such as the prevalence of different disability types, socioeconomic disparities, geographic distribution, and access to essential services like healthcare and education, this research aims to offer a holistic perspective. Recognizing the pivotal role of machine learning, particularly in addressing mental health challenges, this study presents an innovative approach. Utilizing advanced techniques such as Qualitative Analysis, Regression Analysis, and logistic regression, the research lays the groundwork for a proposed system designed to predict and detect parental mental health issues within families raising disabled children. This proposed system integrates classification algorithms, leveraging both structured and unstructured data. For structured data, a combination of sophisticated algorithms including Random Forest, XGboost, KNN, SVM, and decision trees is employed. Meanwhile, Natural Language Processing (NLP) techniques are applied to unstructured data, enhancing the assessment of parental mental health.