A PRISMA-based Comparative Analysis of Machine Learning Techniques in Diagnosing Electrocardiogram, Diabetes, Chronic Kidney Disease, and Breast Cancer

Baljit Kaur; Renu Popli; Hiran Mani Bala

doi:10.2478/ijssis-2026-0013

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A PRISMA-based Comparative Analysis of Machine Learning Techniques in Diagnosing Electrocardiogram, Diabetes, Chronic Kidney Disease, and Breast Cancer

International Journal on Smart Sensing and Intelligent Systems

Volume 19 (2026): Issue 1 (January 2026)

By: Baljit Kaur, Renu Popli and Hiran Mani Bala

Open Access

|Apr 2026

Abstract

Purpose

This paper comprehensively reviews the effects of machine learning (ML) on the diagnosis and treatment of four different diseases: electrocardiogram (ECG), diabetes, chronic kidney disease (CKD), and breast cancer. The primary objective of this paper is to investigate how ML algorithms address challenges related to early detection, diagnostic precision, and data security across various medical fields.

Method

The study employs a systematic review approach, formulating research questions and extensively analyzing existing literature. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology is utilized in the literature on these four diseases. This analysis focused on ML applications in performance metrics, disease prediction, and advancements in the field. The review integrates bibliometrics, PRISMA-based study selection, and an ML technique-based in-depth comparative review.

Results

The review indicates the transformative impact of ML on disease detection and management. Breast cancer research represents the most significant growth and citation rates, indicating its high relevance. CKD and diabetes research also demonstrated notable advancements but faced recent declines in output. However, electrocardiogram research requires further innovation with advancements in other areas.

Conclusion

ML algorithms have great potential to solve data security, early detection capabilities, and diagnostic accuracy problems, and provide new solutions in various medical fields. Future research should focus on improving healthcare delivery and patient outcomes by enhancing model robustness and integrating emerging technologies.

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Articles in this issue

DOI: https://doi.org/10.2478/ijssis-2026-0013 | Journal eISSN: 1178-5608

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Language: English

Submitted on: Dec 9, 2024

Published on: Apr 10, 2026

Published by: Macquarie University, Australia

In partnership with: Paradigm Publishing Services

Publication frequency: 1 issue per year

Keywords:

machine learning,

electrocardiogram,

preferred reporting items for systematic reviews and meta-analyses,

chronic kidney disease

Related subjects:

Engineering,

Introductions and overviews,

Engineering, other

© 2026 Baljit Kaur, Renu Popli, Hiran Mani Bala, published by Macquarie University, Australia
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 19 (2026): Issue 1 (January 2026)