Predictive Healthcare Analytics Using Intelligent Computational Models

Szymon Wilk

Predictive Healthcare Analytics Using Intelligent Computational Models

Author(s)	Szymon Wilk
Country	United States
Abstract	The increasing complexity of healthcare systems, rising prevalence of chronic diseases, and growing availability of medical data have created significant opportunities for predictive healthcare analytics. Traditional healthcare decision-making approaches often depend on historical records, clinical observations, and reactive treatment strategies, which may have limitations in identifying future health risks and supporting proactive medical interventions. Intelligent computational models have emerged as transformative approaches for analysing large-scale healthcare data and improving predictive clinical decision-making. This study examines predictive healthcare analytics using intelligent computational models by analysing their contribution to disease prediction, clinical decision support, patient risk assessment, and healthcare resource optimization. The research explores advanced computational approaches including machine learning algorithms, deep learning models, predictive modelling techniques, artificial intelligence-based analytics, and intelligent healthcare decision-support systems. A simulation-based analytical methodology is developed to compare conventional healthcare analysis methods with intelligent predictive healthcare frameworks using evaluation indicators including prediction accuracy, risk identification capability, decision-support effectiveness, and healthcare optimization performance. The findings demonstrate that intelligent computational models significantly enhance predictive healthcare capabilities by identifying complex medical patterns, forecasting patient risks, and supporting evidence-based clinical decisions. The results indicate that predictive analytics provides valuable opportunities for early diagnosis, personalized treatment planning, and improved healthcare management. However, successful implementation requires addressing challenges related to healthcare data quality, computational complexity, model transparency, clinical validation, ethical considerations, and privacy protection. The study contributes to healthcare analytics research by presenting intelligent computational models as strategic mechanisms for improving future medical decision systems. The integration of advanced analytics, artificial intelligence, and clinical expertise can support more accurate, efficient, and proactive healthcare delivery frameworks.
Keywords	Predictive Healthcare Analytics, Intelligent Computational Models, Artificial Intelligence, Machine Learning, Clinical Decision Support, Medical Data Analytics, Healthcare Prediction.
Field	Engineering
Published In	Volume 6, Issue 6, November-December 2024
Published On	2024-11-13

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American International Journal of Research and Innovation

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Predictive Healthcare Analytics Using Intelligent Computational Models

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