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Machine Learning and Healthcare: A Comprehensive Study

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Communication and Intelligent Systems (ICCIS 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 968))

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Abstract

This paper delves into the dynamic intersection of machine learning (ML) and healthcare, envisioning a paradigm shift in diagnostic accuracy, personalized treatment, and streamlined administration. It meticulously explores various ML algorithms, spanning deep learning, decision trees, and clustering techniques, pivotal in domains like early cancer detection, diabetes detection, heart disease detection, autism spectrum disorder detection, and Parkinson’s disease detection. Rigorous model evaluation, employing accuracy, precision, F1-score, specificity, and mean squared error metrics, ensures algorithm dependability. However, data privacy challenges, amplified by intricate regulations, persist. Ethical considerations add complicated dimensions, including algorithmic bias and cultivating patient trust. Addressing these necessitates robust education for healthcare professionals and alignment with legal frameworks. Despite challenges, the paper advocates for a conscientious integration of ML, emphasizing its transformative potential in healthcare and urging judicious technology amalgamation to propel advancements in patient care and clinical outcomes.

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Authors and Affiliations

  1. Vellore Institute of Technology, Vellore, Tamil Nadu, India

    Riya Raj & Jayakumar Kaliappan

Authors
  1. Riya Raj
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  2. Jayakumar Kaliappan
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Corresponding author

Correspondence to Riya Raj .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Rajasthan Technical University, Kota, Rajasthan, India

    Harish Sharma

  2. Department of Electrical and Electronics Engineering, National Institute of Technology Uttarakhand, Srinagar, Uttarakhand, India

    Vivek Shrivastava

  3. Department of Computer Science, Malaviya National Institute of Technology, Jaipur, Rajasthan, India

    Ashish Kumar Tripathi

  4. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Lipo Wang

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Raj, R., Kaliappan, J. (2024). Machine Learning and Healthcare: A Comprehensive Study. In: Sharma, H., Shrivastava, V., Tripathi, A.K., Wang, L. (eds) Communication and Intelligent Systems. ICCIS 2023. Lecture Notes in Networks and Systems, vol 968. Springer, Singapore. https://doi.org/10.1007/978-981-97-2079-8_3

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