A Systematic Review of Predictive Analytics Applications in Early Disease Detection and Diagnosis
Journal Title: Engineering and Technology Journal - Year 2025, Vol 10, Issue 03
Abstract
The integration of predictive analytics and artificial intelligence (AI) in healthcare has revolutionized early disease detection and diagnosis, significantly improving patient outcomes and reducing healthcare costs. This systematic review examines the applications of predictive analytics in early-stage disease identification, focusing on AI-driven methodologies, machine learning (ML) algorithms, and big data analytics. By leveraging real-time patient data, electronic health records (EHRs), and genomic information, predictive models enhance diagnostic accuracy, facilitate timely interventions, and optimize healthcare resource allocation. The study explores key predictive modeling techniques, including deep learning, natural language processing (NLP), and ensemble learning, which are applied in early detection of diseases such as cancer, cardiovascular disorders, diabetes, and neurodegenerative conditions. The review assesses the effectiveness of supervised and unsupervised learning models in identifying disease markers, analyzing medical imaging, and predicting disease progression. Additionally, AI-powered wearable devices and remote monitoring systems are highlighted for their role in real-time health tracking and early anomaly detection. A critical aspect of this review is evaluating the challenges associated with predictive analytics in healthcare, including data privacy concerns, bias in AI algorithms, integration issues with existing medical systems, and regulatory constraints. The study also discusses emerging trends, such as federated learning and explainable AI, which aim to enhance model transparency, security, and ethical considerations in clinical decision-making. Findings indicate that AI-driven predictive analytics significantly improve disease prognosis, enabling personalized treatment plans and reducing hospital readmissions. However, widespread adoption requires robust validation, interdisciplinary collaboration, and policy advancements to ensure reliability and fairness in AI-based healthcare solutions. This review provides a comprehensive understanding of predictive analytics applications in disease detection and offers insights into future research directions for enhancing AI-driven healthcare innovations.
Authors and Affiliations
Tolulope Olagoke Kolawole , Ashiata Yetunde Mustapha , Akachukwu Obianuju Mbata , Busayo Olamide Tomoh , Adelaide Yeboah Forkuo , Maria Theresa Chinyeaka Kelvin-Agwu,
Keywords
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- EP ID EP762215
- DOI 10.47191/etj/v10i03.35
- Views 12
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