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.
authors.affiliation: Faculty of Computing, Universiti Teknologi Malaysia, Johor Bahru, Malaysia.|Department of Medical Science Technology, Faculty of Applied Medical Science, Majmaah University, Al Majmaah, Kingdom of Saudi Arabia.
authors.forename: Hamdan O
authors.initials: HO
authors.lastname: Alanazi
authors.affiliation: Faculty of Computing, Universiti Teknologi Malaysia, Johor Bahru, Malaysia.
authors.forename: Abdul Hanan
authors.initials: AH
authors.lastname: Abdullah
authors.affiliation: Department of Computer Science, Bahria University, Islamabad, Pakistan. kashifnq@gmail.com.
authors.forename: Kashif Naseer
authors.initials: KN
authors.lastname: Qureshi
mesh_terms: D001185:Artificial Intelligence; D003695:Delivery of Health Care / Q000458:organization & administration*; D006801:Humans; D000069550:Machine Learning; D008962:Models, Theoretical
keywords: Machine learning (ML); Medicine and health care; Predictive model