Machine-Learning-Based Disease Diagnosis: A Comprehensive Review

By M. Ahsan, Zahed Siddique. Healthcare, 2021

527 citations recorded by the source corpus.

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.

Subfields: Health Informatics, Health Information Management, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/870da55ec826bcfeae42ca0f72e06be3660ebcea

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Shuroug A. Alowais
S. Alghamdi
N. Alsuhebany
Abdulrahman I. Alshaya
Sumaya N Almohareb
H. Badreldin
Majed S. Al Yami
Mohammed A. Alrashed

Citations in the corpus

Revolutionizing healthcare: the role of artificial intelligence in clinical practice (2023, background): “These tools are highly applicable in healthcare systems for diagnosing, predicting, or classifying diseases [10].”
ROC Analysis of Biomarker Combinations in Fragile X Syndrome-Specific Clinical Trials: Evaluating Treatment Efficacy via Exploratory Biomarkers (2025, unknown): “Machine learning classi ﬁ ers, most frequently used to identify and separate diagnostic groups via receiver operating curve (ROC) analyses [28], may also be utilized to identify and separate treatment conditions, and can serve as an important screening step for composite biomarker identi ﬁ cation.”