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@article{184358,
        author = {SHARFANA P E and THAJUNNISA N M and BHAVYA P S and SINDHU VENKATESH},
        title = {FINDING HOW STUDENT MENTAL PROBLEM EFFECTS STUDENT ACADEMIC PERFORMANCE USING ML},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {4},
        pages = {1134-1137},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=184358},
        abstract = {Education system plays a vital role in any one’s carrier. Student’s health is an important research topic today because they are the cornerstone of our society. Researchers have employed various technological breakthroughs to address school children’s and college/university learners’ health issues. Machine learning is now frequently employed. However, to understand the efficacy of ML and progress in learner health research, a concise review of the influence of ML on learner health is required, which the proposed work provides. The primary objective is to examine which of the learner’s health concerns are efficiently addressed by ML algorithms and the outcomes of the approaches. The study also discusses what leads learners to perform poorly in schools, colleges, and universities and if ML will improve learner health in the future. The main aim of the study is to find how learner health problems effect their educational outcomes. Unsupervised learning methods are utilized to analyze student-related datasets and identify links between health challenges and academic performance. The model introduces automation within the educational domain and is implemented as a web-based application for college use, developed using Microsoft tools including Visual Studio, C#, and SQL Server.},
        keywords = {},
        month = {September},
        }