Intelligent Health Prediction using Machine Learning

This paper presented a comprehensive study on intelligent health prediction using machine learning techniques. Various approaches, including classical machine learning, ensemble methods, and deep learning architectures, were analyzed and compared. The study highlights that while advanced models provide high accuracy, challenges such as interpretability, data quality, and scalability remain significant barriers to real-world implementation. The integration of Explainable AI techniques is essential for building trust and ensuring transparency in healthcare systems. The proposed methodology provides a structured approach for developing an intelligent health prediction system that can assist in early disease detection and improve healthcare outcomes. Future work will focus on integrating real-time data, improving model generalization, and developing privacy-preserving machine learning techniques such as federated learning.

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