Today's healthcare sector generates an unprecedented amount of data, creating a promising junction between data mining and machine learning. This research aims to achieve two key healthcare goals. First, it effortlessly integrates AI into clinical decision-support systems to improve treatment regimens. The emphasis is on individualizing medicines, increasing effectiveness, and minimizing side effects. This main goal is to optimize treatment methods using AI. The research also examines how data mining and machine learning may improve hospital operations. This objective involves improving logistical administration, planning, and resource allocation to boost operational efficiency, lower healthcare costs, and enhance access to high-quality care. The study rigorously investigates how data-driven approaches may revolutionize healthcare system operations. This study examines the synergy between data-driven methods and medicine, focusing on current trends and advances. The research examines medical applications that demonstrate machine learning's ability to change healthcare delivery. The study aims to illuminate data-driven approaches' promising potential to advance patient-centeredness, financial sustainability, and operational efficiency in healthcare.
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CRediT Author Statement
The authors confirm contribution to the paper as follows:
Conceptualization: Purna Chandra Rao Kandimalla, Anuradha T;
Methodology: Purna Chandra Rao Kandimalla, Anuradha T;
Visualization: Purna Chandra Rao Kandimalla;
Validation: Anuradha T;
All authors reviewed the results and approved the final version of the manuscript.
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Author(s) thanks to Dr. Anuradha T for this research completion and support.
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Purna Chandra Rao Kandimalla
Department of Computer Science and Engineering, Acharya Nagarjuna University, Guntur, Andhra Pradesh, India.
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Cite this article
Purna Chandra Rao Kandimalla and Anuradha T, “Data-Driven Innovations: Transforming Healthcare through Machine Learning Integration”, Journal of Machine and Computing, vol.5, no.1, pp. 356-364, January 2025, doi: 10.53759/7669/jmc202505027.
