e-space
Manchester Metropolitan University's Research Repository

    A comprehensive review on medical diagnosis using machine learning

    Bhavsar, Kaustubh Arun, Abugabah, Ahed, Singla, Jimmy, AlZubi, Ahmad Ali, Bashir, Ali Kashif ORCID logoORCID: https://orcid.org/0000-0001-7595-2522 and Nikita (2021) A comprehensive review on medical diagnosis using machine learning. Computers, Materials and Continua, 67 (2). pp. 1997-2014. ISSN 1546-2218

    [img]
    Preview
    Published Version
    Available under License Creative Commons Attribution.

    Download (568kB) | Preview

    Abstract

    The unavailability of sufficient information for proper diagnosis, incomplete or miscommunication between patient and the clinician, or among the healthcare professionals, delay or incorrect diagnosis, the fatigue of clinician, or even the high diagnostic complexity in limited time can lead to diagnostic errors. Diagnostic errors have adverse effects on the treatment of a patient. Unnecessary treatments increase the medical bills and deteriorate the health of a patient. Such diagnostic errors that harm the patient in various ways could be minimized using machine learning. Machine learning algorithms could be used to diagnose various diseases with high accuracy. The use of machine learning could assist the doctors in making decisions on time, and could also be used as a second opinion or supporting tool. This study aims to provide a comprehensive review of research articles published from the year 2015 to mid of the year 2020 that have used machine learning for diagnosis of various diseases. We present the various machine learning algorithms used over the years to diagnose various diseases. The results of this study show the distribution of machine learningmethods by medical disciplines. Based on our review, we present future research directions that could be used to conduct further research.

    Impact and Reach

    Statistics

    Activity Overview
    6 month trend
    493Downloads
    6 month trend
    180Hits

    Additional statistics for this dataset are available via IRStats2.

    Altmetric

    Repository staff only

    Edit record Edit record