The recent advancements in machine learning and artificial intelligence are significantly transforming various domains, with a particular emphasis on healthcare, education, and sports. In healthcare, there is a notable shift towards non-invasive diagnostic tools powered by AI, which are improving the accuracy and efficiency of detecting and staging diseases like NAFLD. These innovations not only reduce the healthcare burden but also enhance patient outcomes by enabling early intervention. In education, the focus is on predicting and supporting student performance through advanced neural network models, which are capable of forecasting multi-category outcomes and identifying at-risk students early in their academic journey. This proactive approach aims to improve retention and academic success rates. In sports, there is a growing interest in real-time psychological state monitoring for athletes, leveraging hybrid models to provide personalized interventions that enhance performance and mental well-being. Overall, the integration of AI and machine learning in these fields is driving a paradigm shift towards more personalized, efficient, and proactive solutions.
AI-Driven Innovations in Healthcare, Education, and Sports
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Multi-class heart disease Detection, Classification, and Prediction using Machine Learning Models
AI-Driven Non-Invasive Detection and Staging of Steatosis in Fatty Liver Disease Using a Novel Cascade Model and Information Fusion Techniques
Risk factor identification and classification of malnutrition among under-five children in Bangladesh: Machine learning and statistical approach
Real-Time Prediction for Athletes' Psychological States Using BERT-XGBoost: Enhancing Human-Computer Interaction
Accurate Multi-Category Student Performance Forecasting at Early Stages of Online Education Using Neural Networks
Machine Learning Algorithms for Detecting Mental Stress in College Students
Early Detection of At-Risk Students Using Machine Learning