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Review
. 2022 Mar 15;10(3):541.
doi: 10.3390/healthcare10030541.

Machine-Learning-Based Disease Diagnosis: A Comprehensive Review

Md Manjurul Ahsan  1 , Shahana Akter Luna  2 , Zahed Siddique  3
Affiliations
Review

Machine-Learning-Based Disease Diagnosis: A Comprehensive Review

Md Manjurul Ahsan et al. Healthcare (Basel). .

Abstract

Globally, there is a substantial unmet need to diagnose various diseases effectively. The complexity of the different disease mechanisms and underlying symptoms of the patient population presents massive challenges in developing the early diagnosis tool and effective treatment. Machine learning (ML), an area of artificial intelligence (AI), enables researchers, physicians, and patients to solve some of these issues. Based on relevant research, this review explains how machine learning (ML) is being used to help in the early identification of numerous diseases. Initially, a bibliometric analysis of the publication is carried out using data from the Scopus and Web of Science (WOS) databases. The bibliometric study of 1216 publications was undertaken to determine the most prolific authors, nations, organizations, and most cited articles. The review then summarizes the most recent trends and approaches in machine-learning-based disease diagnosis (MLBDD), considering the following factors: algorithm, disease types, data type, application, and evaluation metrics. Finally, in this paper, we highlight key results and provides insight into future trends and opportunities in the MLBDD area.

Keywords: COVID-19; artificial neural networks; convolutional neural networks; deep learning; deep neural networks; diabetes; disease diagnosis; heart disease; kidney disease; machine learning; review.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Different types of machine learning algorithms.
Figure 2
Figure 2
Some of the most well-known CNN models, along with their development time frames.
Figure 3
Figure 3
Illustration of machine learning and deep learning algorithms development timeline.
Figure 4
Figure 4
MLBDD article selection procedure used in this study.
Figure 5
Figure 5
Distribution of articles by subject area.
Figure 6
Figure 6
Bibliometric map representing co-occurrence analysis of keywords in network visualization.
Figure 7
Figure 7
Publications of machine-learning-based disease diagnosis (MLBDD) by year.
Figure 8
Figure 8
Publications by journals.
Figure 9
Figure 9
Top ten countries that contributed to MLBDD literature.
Figure 10
Figure 10
Word cloud for most frequently used ML algorithms in MLBDD publications.
See this image and copyright information in PMC

References

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    1. Ahsan M.M., Ahad M.T., Soma F.A., Paul S., Chowdhury A., Luna S.A., Yazdan M.M.S., Rahman A., Siddique Z., Huebner P. Detecting SARS-CoV-2 From Chest X-ray Using Artificial Intelligence. IEEE Access. 2021;9:35501–35513. doi: 10.1109/ACCESS.2021.3061621. - DOI - PMC - PubMed
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    1. Balogh E.P., Miller B.T., Ball J.R. Improving Diagnosis in Health Care. National Academic Press; Washington, DC, USA: 2015. - DOI - PubMed
    1. Ahsan M.M., Siddique Z. Machine Learning-Based Heart Disease Diagnosis: A Systematic Literature Review. arXiv. 20212112.06459 - PubMed

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