Machine Learning Models for Predictive Healthcare Analytics

Víctor Maojo García

Machine Learning Models for Predictive Healthcare Analytics

Author(s)	Víctor Maojo García
Country	Spain
Abstract	Predictive healthcare analytics has increasingly developed through the application of machine learning models to clinical, administrative, and patient-generated data. Machine learning can identify complex patterns within healthcare datasets and support prediction of disease risk, patient outcomes, hospital utilization, treatment response, and resource requirements. This study examines machine learning models for predictive healthcare analytics, focusing on supervised learning, unsupervised learning, ensemble methods, deep learning, predictive modeling, clinical decision support, and healthcare data management. A qualitative descriptive and conceptual research design was adopted using secondary academic literature. The analysis indicates that machine learning can support predictive healthcare by transforming historical and real-time healthcare data into clinically relevant predictions. Decision trees, logistic regression, support vector machines, random forests, gradient-boosting approaches, neural networks, and deep-learning models provide different capabilities for healthcare prediction. Their effectiveness depends on data quality, feature selection, model validation, interpretability, computational requirements, and appropriate clinical integration. Predictive analytics can support early risk identification, personalized care planning, patient monitoring, readmission prediction, disease progression assessment, and healthcare resource management. However, challenges involving data heterogeneity, missing information, algorithmic bias, privacy, cybersecurity, model interpretability, clinical validation, and regulatory requirements remain important. The study concludes that machine learning models can strengthen predictive healthcare analytics when developed and implemented through reliable, transparent, clinically relevant, and ethically responsible frameworks. Future research should emphasize explainable artificial intelligence, multimodal healthcare data, federated learning, fairness-aware modeling, continuous validation, privacy-preserving analytics, and human-centered clinical decision support.
Keywords	Machine Learning, Predictive Healthcare Analytics, Healthcare Data, Artificial Intelligence, Clinical Decision Support, Predictive Modeling, Deep Learning, Healthcare Informatics, Disease Prediction, Health Analytics
Field	Engineering
Published In	Volume 5, Issue 2, March-April 2023
Published On	2023-03-06

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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

Machine Learning Models for Predictive Healthcare Analytics

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