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<p>Big data analytics is reshaping healthcare by integrating machine learning with large medical datasets. It enables predictive diagnosis, personalized treatment, and real-time monitoring through telemedicine and wearable devices, improving decision-making and extending care to underserved populations. This survey reviews applications of big data in enhancing diagnosis accuracy, chronic disease management, and resource optimization. Predictive models for heart disease and stroke demonstrate significant improvements in timely intervention and patient outcomes. However, challenges such as poor data quality, privacy concerns, interoperability issues, and connectivity gaps hinder widespread adoption. Ethical considerations around patient confidentiality also remain critical. The paper compares existing big data technologies and machine learning frameworks, highlighting their strengths, limitations, and clinical impact. It concludes with recommendations for advancing predictive analytics, personalized medicine, and scalable, data-driven healthcare systems to guide researchers, practitioners, and policymakers.</p>

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Role of big data analysis to improve the efficiency of healthcare system using machine learning techniques

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月30日

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  • Ajay Singh Mavai,
  • Devendra Kumar Mishra,
  • Abhishek Sharma,
  • Sulabh Bansal

摘要

Big data analytics is reshaping healthcare by integrating machine learning with large medical datasets. It enables predictive diagnosis, personalized treatment, and real-time monitoring through telemedicine and wearable devices, improving decision-making and extending care to underserved populations. This survey reviews applications of big data in enhancing diagnosis accuracy, chronic disease management, and resource optimization. Predictive models for heart disease and stroke demonstrate significant improvements in timely intervention and patient outcomes. However, challenges such as poor data quality, privacy concerns, interoperability issues, and connectivity gaps hinder widespread adoption. Ethical considerations around patient confidentiality also remain critical. The paper compares existing big data technologies and machine learning frameworks, highlighting their strengths, limitations, and clinical impact. It concludes with recommendations for advancing predictive analytics, personalized medicine, and scalable, data-driven healthcare systems to guide researchers, practitioners, and policymakers.