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

Hamdan O. Alanazi, Abdul Hanan Abdullah, Kashif Naseer Qureshi

Research output: Research - peer-reviewArticle

  • 1 Citations

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.

LanguageEnglish
Article number69
JournalJournal of Medical Systems
Volume41
Issue number4
DOIs
StatePublished - 1 Apr 2017

Fingerprint

Medicine
Delivery of Health Care
Machine Learning
Health care
Learning systems
Artificial Intelligence
Artificial intelligence
Health Care Sector
Therapeutics
Datasets

Keywords

  • Machine learning (ML)
  • Medicine and health care
  • Predictive model

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • Information Systems
  • Health Informatics
  • Health Information Management

Cite this

A Critical Review for Developing Accurate and Dynamic Predictive Models Using Machine Learning Methods in Medicine and Health Care. / Alanazi, Hamdan O.; Abdullah, Abdul Hanan; Qureshi, Kashif Naseer.

In: Journal of Medical Systems, Vol. 41, No. 4, 69, 01.04.2017.

Research output: Research - peer-reviewArticle

@article{8ee512cfd18d448798cc85d3e61dd713,
title = "A Critical Review for Developing Accurate and Dynamic Predictive Models Using Machine Learning Methods in Medicine and Health Care",
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.",
keywords = "Machine learning (ML), Medicine and health care, Predictive model",
author = "Alanazi, {Hamdan O.} and Abdullah, {Abdul Hanan} and Qureshi, {Kashif Naseer}",
year = "2017",
month = "4",
doi = "10.1007/s10916-017-0715-6",
volume = "41",
journal = "Journal of Medical Systems",
issn = "0148-5598",
publisher = "Springer New York",
number = "4",

}

TY - JOUR

T1 - A Critical Review for Developing Accurate and Dynamic Predictive Models Using Machine Learning Methods in Medicine and Health Care

AU - Alanazi,Hamdan O.

AU - Abdullah,Abdul Hanan

AU - Qureshi,Kashif Naseer

PY - 2017/4/1

Y1 - 2017/4/1

N2 - 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.

AB - 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.

KW - Machine learning (ML)

KW - Medicine and health care

KW - Predictive model

UR - http://www.scopus.com/inward/record.url?scp=85015047040&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85015047040&partnerID=8YFLogxK

U2 - 10.1007/s10916-017-0715-6

DO - 10.1007/s10916-017-0715-6

M3 - Article

VL - 41

JO - Journal of Medical Systems

T2 - Journal of Medical Systems

JF - Journal of Medical Systems

SN - 0148-5598

IS - 4

M1 - 69

ER -