A critical review for developing accurate and dynamic predictive models using machine learning methods in medicine and health care
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 pr...
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| Main Authors: | , , |
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| Format: | Article |
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Springer New York LLC
2017
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| Online Access: | http://eprints.utm.my/id/eprint/76506/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85015047040&doi=10.1007%2fs10916-017-0715-6&partnerID=40&md5=3a6e500473d1a9f3481dba797c07310e |
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| Summary: | 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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