Applications of Machine Learning Predictive Models in the Chronic Disease Diagnosis

This paper reviews applications of machine learning (ML) predictive models in the diagnosis of chronic diseases. Chronic diseases (CDs) are responsible for a major portion of global health costs. Patients who suffer from these diseases need lifelong treatment. Nowadays, predictive models are frequen...

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Bibliographic Details
Published in:	Journal of personalized medicine Vol. 10; no. 2; p. 21
Main Authors:	Battineni, Gopi, Sagaro, Getu Gamo, Chinatalapudi, Nalini, Amenta, Francesco
Format:	Journal Article
Language:	English
Published:	Switzerland MDPI AG 31-03-2020 MDPI
Subjects:	Accuracy Artificial intelligence Automation Chronic illnesses Chronic obstructive pulmonary disease Decision making Diabetes Diagnosis Disease Learning algorithms Liver Machine learning Medicine Neural networks Nursing Patients Precision medicine Prediction models Quality Review Studies Support vector machines prediction models accuracy disease classification chronic diseases pathologies
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Summary:	This paper reviews applications of machine learning (ML) predictive models in the diagnosis of chronic diseases. Chronic diseases (CDs) are responsible for a major portion of global health costs. Patients who suffer from these diseases need lifelong treatment. Nowadays, predictive models are frequently applied in the diagnosis and forecasting of these diseases. In this study, we reviewed the state-of-the-art approaches that encompass ML models in the primary diagnosis of CD. This analysis covers 453 papers published between 2015 and 2019, and our document search was conducted from PubMed (Medline), and Cumulative Index to Nursing and Allied Health Literature (CINAHL) libraries. Ultimately, 22 studies were selected to present all modeling methods in a precise way that explains CD diagnosis and usage models of individual pathologies with associated strengths and limitations. Our outcomes suggest that there are no standard methods to determine the best approach in real-time clinical practice since each method has its advantages and disadvantages. Among the methods considered, support vector machines (SVM), logistic regression (LR), clustering were the most commonly used. These models are highly applicable in classification, and diagnosis of CD and are expected to become more important in medical practice in the near future.
Bibliography:	ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-3 content type line 23 ObjectType-Review-1
ISSN:	2075-4426 2075-4426
DOI:	10.3390/jpm10020021