Predictive Healthcare Models Using Advanced Data Analytics

Tawanda Mushiri

Predictive Healthcare Models Using Advanced Data Analytics

Author(s)	Tawanda Mushiri
Country	Zimbabwe
Abstract	Predictive healthcare models supported by advanced data analytics are increasingly important for improving disease-risk assessment, patient monitoring, clinical decision-making, and healthcare resource management. The growing availability of electronic health records, medical imaging, laboratory information, genomic datasets, wearable-device measurements, and longitudinal patient information has created opportunities for developing analytical models capable of identifying clinically relevant patterns. This study examines predictive healthcare models using advanced data analytics, with emphasis on machine learning, statistical modeling, deep learning, predictive classification, risk estimation, and healthcare decision support. A qualitative descriptive and conceptual research design was adopted using secondary academic literature. The analysis indicates that predictive healthcare models can support early disease identification, patient-risk stratification, readmission prediction, clinical outcome estimation, personalized healthcare, and resource planning. Advanced data analytics can process large and heterogeneous datasets and identify relationships that may not be easily detected through conventional analytical methods. However, successful implementation is influenced by data quality, missing information, algorithmic bias, interpretability, privacy, cybersecurity, interoperability, model generalizability, and clinical validation. The study concludes that predictive healthcare models can strengthen data-driven healthcare when developed using representative datasets, appropriate analytical techniques, rigorous validation, and clinically meaningful interpretation. Future research should emphasize multimodal data integration, explainable predictive models, real-time analytics, privacy-preserving learning, personalized prediction, and prospective clinical evaluation.
Keywords	Predictive Healthcare, Data Analytics, Machine Learning, Healthcare Prediction, Clinical Decision Support, Predictive Modeling, Electronic Health Records, Healthcare Informatics, Risk Assessment, Artificial Intelligence
Field	Engineering
Published In	Volume 5, Issue 3, May-June 2023
Published On	2023-06-02

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American International Journal of Advanced Research and Development

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Predictive Healthcare Models Using Advanced Data Analytics

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