AI-Driven Disease Prediction Framework for Healthcare Applications

Abstract

The rapid advancement of artificial intelligence (AI) has transformed healthcare by enabling more accurate, efficient, and proactive disease management. This study proposes an AI-driven disease prediction framework designed to enhance early diagnosis and improve patient outcomes. The framework integrates machine learning algorithms, including supervised and ensemble models, with patient health data collected from electronic health records (EHRs), wearable devices, and other medical sources. By leveraging techniques such as feature selection, anomaly detection, and predictive analytics, the system identifies patterns and risk factors associated with various diseases. Experimental evaluation demonstrates that the proposed framework achieves high accuracy, precision, and recall across multiple disease categories, outperforming traditional diagnostic methods. The findings indicate that AI-driven predictive models can support clinicians in decision-making, optimize healthcare resource allocation, and facilitate personalized treatment strategies. This research underscores the potential of AI frameworks to transform healthcare from reactive care to proactive and preventive medicine.

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