Abstract: The education system plays a crucial role in shaping individuals' careers, and students' health is an increasingly significant research topic because they are the foundation of our society. Researchers have leveraged various technological advancements to address health issues among schoolchildren and college/university students, with machine learning becoming a commonly used tool. However, to gauge the effectiveness of machine learning and advancements in student health research, a concise review of its impact on student health is needed, which the proposed work aims to provide. The primary objective is to analyze which student health concerns are effectively addressed by machine learning algorithms and the outcomes of these approaches. The project also explores the factors that contribute to poor academic performance in schools, colleges, and universities, and whether machine learning can enhance student health in the future. The main aim of the project is to determine how student health problems affect their academic performance. Unsupervised learning algorithms are applied to process educational data and generate correlations between student health issues and academic performance. In this proposed system, we develop automation for the education sector. The proposed system is a browser-based application designed for a college, developed using Microsoft technologies such as Visual Studio, C#, and SQL Server.

Keywords: Education system, Student health, Machine Learning, Health issues, Correlation, Browser-based application.

| DOI: 10.17148/IARJSET.2024.11567