Abstract

Predictive analytics, underpinned by evidence-based big data analysis, has emerged as a transformative approach to disease detection, enabling earlier diagnosis, personalized care, and improved healthcare outcomes. This paper explores a conceptual framework for predictive disease detection, focusing on critical components such as data collection, preprocessing, modeling, validation, and deployment. It highlights the role of big data, characterized by volume, velocity, variety, veracity, and value, in enriching predictive models, while addressing the integration of machine learning and statistical techniques to enhance precision and interpretability. Challenges such as data fragmentation, privacy concerns, and algorithmic bias are examined alongside ethical considerations, emphasizing fairness and transparency. Recommendations for future research include advancing data interoperability, mitigating bias, and prioritizing explainable artificial intelligence. By leveraging these insights, healthcare systems can effectively harness predictive analytics for early intervention, equitable care, and improved patient outcomes, marking a significant step forward in modern medicine.