Abstract: Machine learning (ML) has emerged as a transformative force in the healthcare sector, enabling advanced data analysis, improved diagnostic accuracy, and efficient clinical decision-making. The rapid digitization of healthcare systems has resulted in the generation of vast amounts of structured and unstructured data from sources such as electronic health records, medical imaging technologies, wearable devices, and laboratory systems. Traditional analytical approaches often struggle to manage and interpret such complex datasets effectively. In this context, machine learning provides powerful computational techniques that can identify hidden patterns, predict outcomes, and support medical professionals in delivering high-quality care. This paper presents a comprehensive review of machine learning applications in healthcare, covering areas such as disease diagnosis, predictive analytics, medical imaging, and healthcare operations. It also describes a structured methodology for developing machine learning models, including data preparation, preprocessing, model selection, and evaluation. Experimental results based on a synthetic dataset are discussed to illustrate model performance. Furthermore, the study highlights key challenges such as data privacy, model interpretability, and implementation barriers. The paper concludes by discussing future research directions aimed at enhancing the reliability and adoption of machine learning in healthcare systems.

Keywords: Machine Learning, Healthcare Analytics, Artificial Intelligence, Predictive Modeling, Medical Imaging, Natural Language Processing, Clinical Decision Support

Downloads: | DOI: 10.17148/IJIREEICE.2026.14437