Abstract
The convergence of large-scale electronic health records (EHR), wearable biosensors, and modern machine learning (ML) algorithms has fundamentally transformed how clinicians anticipate and manage disease. Predictive analysis in healthcare refers to the systematic application of statistical and computational models to historical and real-time patient data to forecast clinical outcomes, optimize resource allocation, and personalise therapeutic interventions. This paper presents a comprehensive investigation of predictive modelling methodologies employed in healthcare contexts, examining classical regression techniques, ensemble methods, deep learning architectures, and explainable artificial intelligence (XAI) frameworks. Four high-impact application domains are explored in depth: early disease detection, hospital readmission prediction, mortality risk stratification, and epidemic surveillance. A comparative evaluation of model performance across published benchmarks is synthesised, and the primary challenges of data heterogeneity, class imbalance, interpretability, and regulatory compliance are critically analysed. The paper concludes with an evidence-based roadmap for the responsible deployment of predictive analytics in clinical practice.
Citations
IRE Journals:
Mohd Zaid Arif, Zakiullah Siddiqui "Predictive Analysis in Healthcare: Techniques, Applications, and Future Directions" Iconic Research And Engineering Journals Volume 9 Issue 10 2026 Page 1376-1380 https://doi.org/10.64388/IREV9I10-1716266
IEEE:
Mohd Zaid Arif, Zakiullah Siddiqui
"Predictive Analysis in Healthcare: Techniques, Applications, and Future Directions" Iconic Research And Engineering Journals, 9(10) https://doi.org/10.64388/IREV9I10-1716266
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