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The growing population and the paramount importance of health in society have underscored the need to manage and analyze extensive patient data. Healthcare is a top priority, but it grapples with escalating costs in areas like disease diagnosis, prediction, drug discovery, medical imaging interpretation, personalized medicine, behavioral therapy, and digital health records. Machine learning emerges as a crucial tool for processing this data, elevating the efficiency of healthcare systems. Accurate diagnosis is pivotal in healthcare, offering vital insights into a patient's condition and guiding treatment decisions. Disease diagnosis is a complex and collaborative process, involving the collection and analysis of clinical, intelligent, and data-driven information to reach conclusive diagnoses. Machine Learning, a subset of Artificial Intelligence, continuously learns and refines itself through experience. It extends its applications beyond healthcare to other sectors such as law, marketing, finance, retail, customer services, and addressing healthcare challenges like those posed by Covid-19. Additionally, ML-driven techniques aid in the early detection of epidemic or pandemic indicators by analyzing satellite data, news reports, and social media, potentially preventing outbreaks. The integration of ML into healthcare opens doors to various possibilities, freeing healthcare providers from administrative burdens to focus on patient care. This paper delves into the essential role of ML in healthcare, exploring its core components and highlighting key applications. Implementing ML in healthcare operations can significantly benefit organizations by offering diverse treatment options, personalized care, and enhancing overall system efficiency while reducing costs.
Kaur et al. (Thu,) studied this question.