Authors: Sreehari K B, Deepakumar M
Abstract: Early disease prediction is a crucial aspect of modern healthcare systems, as it enables timely medical intervention, improves patient survival rates, and reduces long-term healthcare costs. Many chronic and life-threatening diseases such as diabetes, cardiovascular disorders, cancer, and neurological conditions develop gradually and often remain asymptomatic during their early stages. Traditional diagnostic approaches, which rely on clinical rules, physician experience, and fixed statistical thresholds, are often inadequate for detecting these early-stage disease patterns. and neurological disorders progress slowly over time and are often diagnosed only at advanced stages. Late diagnosis significantly reduces treatment effectiveness and increases mortality rates. With the growing global disease burden and aging population, early detection has become a priority in modern healthcare systems. Advancements in healthcare digitization have led to the availability of large-scale medical data, including Electronic Health Records (EHRs), laboratory reports, and medical imaging. These datasets provide valuable insights into patient health patterns and disease progression, enabling the development of predictive models for early diagnosis. With the rapid digitization of healthcare, vast amounts of medical data are generated through Electronic Health Records (EHRs), laboratory test reports, diagnostic imaging, and wearable health devices. This has created opportunities for Artificial Intelligence (AI) and Machine Learning (ML) techniques to analyze complex and high- dimensional medical data efficiently. Existing AI- based disease prediction systems have demonstrated improved accuracy compared to conventional methods; however, many of these systems suffer from limitations such as reliance on single-modal data, centralized data storage, poor generalization across healthcare institutions, severe class imbalance, and lack of interpretability. This project proposes an AI-based early disease prediction framework that addresses these limitations through the integration of multimodal clinical data, privacy-aware learning mechanisms, imbalance-sensitive training strategies, and explainable AI techniques. The proposed system learns complex patterns from longitudinal patient data and generates calibrated risk scores to support early diagnosis and preventive care. By improving transparency, robustness, and clinical trust, the proposed framework aims to provide an effective and scalable solution for early disease prediction in real-world healthcare environments.
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