Revolutionizing Student Health: A Review of Deep Learning Applications in Early Diagnosis and Personalized Monitoring

Abstract

The integration of deep learning methodologies with predictive analytics has demonstrated significant promise in enhancing student health outcomes. This study offers a comprehensive analysis of contemporary trends in predictive analytics and the implementation of deep learning methodologies. The examined studies indicate that deep learning models, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), display significant accuracy and efficiency in early disease detection, mental health forecasting, and individualized health monitoring. Significant findings encompass the enhanced prediction precision of hybrid models, the proficiency of deep learning in managing intricate and sequential data, and the opportunity for early intervention via tailored health insights. Nonetheless, obstacles include inadequate data quality, algorithmic biases, and model interpretability persist as significant concerns. The implementation of deep learning models necessitates ethical considerations and openness. Subsequent research ought to concentrate on tackling these problems and broadening the utilization of deep learning across varied student demographics and health circumstances. The results indicate that deep learning can markedly improve early diagnosis, treatment optimization, and overall health outcomes for students, presenting a promising strategy for enhancing student health.