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The Significance of Machine Learning in Clinical Disease Diagnosis: A Review

by SM Atikur Rahman, Sifat Ibtisum, Ehsan Bazgir, Tumpa Barai
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 185 - Number 36
Year of Publication: 2023
Authors: SM Atikur Rahman, Sifat Ibtisum, Ehsan Bazgir, Tumpa Barai
10.5120/ijca2023923147

SM Atikur Rahman, Sifat Ibtisum, Ehsan Bazgir, Tumpa Barai . The Significance of Machine Learning in Clinical Disease Diagnosis: A Review. International Journal of Computer Applications. 185, 36 ( Oct 2023), 10-17. DOI=10.5120/ijca2023923147

@article{ 10.5120/ijca2023923147,
author = { SM Atikur Rahman, Sifat Ibtisum, Ehsan Bazgir, Tumpa Barai },
title = { The Significance of Machine Learning in Clinical Disease Diagnosis: A Review },
journal = { International Journal of Computer Applications },
issue_date = { Oct 2023 },
volume = { 185 },
number = { 36 },
month = { Oct },
year = { 2023 },
issn = { 0975-8887 },
pages = { 10-17 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume185/number36/32921-2023923147/ },
doi = { 10.5120/ijca2023923147 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:27:55.066547+05:30
%A SM Atikur Rahman
%A Sifat Ibtisum
%A Ehsan Bazgir
%A Tumpa Barai
%T The Significance of Machine Learning in Clinical Disease Diagnosis: A Review
%J International Journal of Computer Applications
%@ 0975-8887
%V 185
%N 36
%P 10-17
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The global need for effective disease diagnosis remains substantial, given the complexities of various disease mechanisms and diverse patient symptoms. To tackle these challenges, researchers, physicians, and patients are turning to machine learning (ML), an artificial intelligence (AI) discipline, to develop solutions. By leveraging sophisticated ML and AI methods, healthcare stakeholders gain enhanced diagnostic and treatment capabilities. However, there is a scarcity of research focused on ML algorithms for enhancing the accuracy and computational efficiency. This research investigates the capacity of machine learning algorithms to improve the transmission of heart rate data in time series healthcare metrics, concentrating particularly on optimizing accuracy and efficiency. By exploring various ML algorithms used in healthcare applications, the review presents the latest trends and approaches in ML-based disease diagnosis (MLBDD). The factors under consideration include the algorithm utilized, the types of diseases targeted, the data types employed, the applications, and the evaluation metrics. This review aims to shed light on the prospects of ML in healthcare, particularly in disease diagnosis. By analyzing the current literature, the study provides insights into state-of-the-art methodologies and their performance metrics.

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Index Terms

Computer Science
Information Sciences

Keywords

Machine learning (ML) IoMT healthcare supervised learning chronic kidney disease (CKD) convolutional neural networks adaptive boosting (AdaBoost) COVID-19 deep learning (DL).

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