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Journal of Medical Systems

Erschienen in:

01.04.2017 | Systems-Level Quality Improvement

A Critical Review for Developing Accurate and Dynamic Predictive Models Using Machine Learning Methods in Medicine and Health Care

verfasst von: Hamdan O. Alanazi, Abdul Hanan Abdullah, Kashif Naseer Qureshi

Erschienen in: Journal of Medical Systems | Ausgabe 4/2017

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Abstract

Recently, Artificial Intelligence (AI) has been used widely in medicine and health care sector. In machine learning, the classification or prediction is a major field of AI. Today, the study of existing predictive models based on machine learning methods is extremely active. Doctors need accurate predictions for the outcomes of their patients’ diseases. In addition, for accurate predictions, timing is another significant factor that influences treatment decisions. In this paper, existing predictive models in medicine and health care have critically reviewed. Furthermore, the most famous machine learning methods have explained, and the confusion between a statistical approach and machine learning has clarified. A review of related literature reveals that the predictions of existing predictive models differ even when the same dataset is used. Therefore, existing predictive models are essential, and current methods must be improved.

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Titel: A Critical Review for Developing Accurate and Dynamic Predictive Models Using Machine Learning Methods in Medicine and Health Care
verfasst von: Hamdan O. Alanazi
Abdul Hanan Abdullah
Kashif Naseer Qureshi
Publikationsdatum: 01.04.2017
Verlag: Springer US
Erschienen in: Journal of Medical Systems / Ausgabe 4/2017
Print ISSN: 0148-5598
Elektronische ISSN: 1573-689X
DOI: https://doi.org/10.1007/s10916-017-0715-6

Springer Medizin

Abstract

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