Applications of Machine Learning Predictive Models in the Chronic Disease Diagnosis - PubMed Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2020 Mar 31;10(2):21.
doi: 10.3390/jpm10020021.

Applications of Machine Learning Predictive Models in the Chronic Disease Diagnosis

Gopi Battineni  1 , Getu Gamo Sagaro  1 , Nalini Chinatalapudi  1 , Francesco Amenta  1   2
Affiliations
Review

Applications of Machine Learning Predictive Models in the Chronic Disease Diagnosis

Gopi Battineni et al. J Pers Med. .

Abstract

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.

Keywords: accuracy; chronic diseases; disease classification; pathologies; prediction models.

PubMed Disclaimer

Conflict of interest statement

Authors do not have any potential conflicts of interest.

Figures

Figure 1
Figure 1
Preferred reporting items for systematic reviews and meta-analyses (PRISMA) diagram [15].
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Hamet P., Tremblay J. Artificial intelligence in medicine. Metabolism. 2017;69:S36–S40. doi: 10.1016/j.metabol.2017.01.011. - DOI - PubMed
    1. Johnson K.W., Soto J.T., Glicksberg B.S., Shameer K., Miotto R., Ali M., Dudley J.T. Artificial intelligence in cardiology. J. Am. Coll. Cardiol. 2018;71:2668–2679. doi: 10.1016/j.jacc.2018.03.521. - DOI - PubMed
    1. Bini S. Artificial Intelligence, Machine Learning, Deep Learning, and Cognitive Computing: What Do These Terms Mean and How Will They Impact Health Care? J. Arthroplast. 2018;33:2358–2361. doi: 10.1016/j.arth.2018.02.067. - DOI - PubMed
    1. Buch V.H., Ahmed I., Maruthappu M. Artificial intelligence in medicine: Current trends and future possibilities. Br. J. Gen. Pract. 2018;68:143–144. doi: 10.3399/bjgp18X695213. - DOI - PMC - PubMed
    1. Kotsiantis S.B., Zaharakis I., Pintelas P. Supervised machine learning: A review of classification techniques. Emerg. Artif. Intell. Appl. Comput. Eng. 2007;160:3–24.

LinkOut - more resources