A Comprehensive Review On The Analysis Of Various Machine Learning Algorithms For Early Detection Of Critical Diseases

Aug 4, 2026

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Early detection of critical diseases is essential for improving patient outcomes and reducing healthcare costs. This paper reviews and analyzes machine learning algorithms used for early disease detection, comparing Logistic Regression, Support Vector Machines, Random Forests, Neural Networks, K-Nearest Neighbors, and Ensemble Learning. For each method, the review evaluates interpretability, scalability, and performance across diverse data types. It also discusses medical applications in different contexts and synthesizes current research to support researchers and policymakers advancing ML-driven early identification of critical diseases.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages8

Reading Time20 min

AccessFree to download

Introduction

Literature Review

Review Scope & Algorithm Assessment

Journal of Advanced Zoology ISSN: 0253-7214 Volume 45 IssueS-4 Year 2024 Page 65-72 A Comprehensive Review On The Analysis Of Various Machine Learning Algorithms For Early Detection Of Critical Diseases Divya Chitre1*, Prof. Shivendu Bhushan2, Dr. Manisha S Patil3 1,* 2,3Indira college of commerce and Science. [Email: divya.chitre@iccs.ac.in](Email: divya.chitre@iccs.ac.in1)[1](Email: divya.chitre@iccs.ac.in1), [shivendu@iccs.ac.in](shivendu@iccs.ac.in2)[2](shivendu@iccs.ac.in2), [manisha.patil@issc.ac.in](manisha.patil@issc.ac.in3)[3](manisha.patil@issc.ac.in3) *Correspondence Author: Divya Chitre * Indira college of commerce and Science. [Email: divya.chitre@iccs.ac.in](Email: divya.chitre@iccs.ac.in) | CC License
CC-BY-NC-SA 4.0 | Abstract
Early detection of critical diseases is a pivotal aspect of modern healthcare, significantly impacting patient outcomes and healthcare costs. This research paper provides a comprehensive review and analysis of various machine learning algorithms employed in the realm of early disease detection. The study explores the strengths, limitations, and overall efficacy of prominent algorithms, including Logistic Regression, Support Vector Machines, Random Forests, Neural Networks, K-Nearest Neighbors, and Ensemble Learning. Each algorithm's suitability for early detection is assessed based on factors such as interpretability, scalability, and performance in handling diverse data types. Furthermore, the review discusses the specific applications of these algorithms in different medical contexts, highlighting their contributions to the early identification of critical diseases. By synthesizing the current state of research, this paper aims to provide valuable insights for researchers, and policymakers working towards advancing the field of early disease detection through machine learning.
Keywords: Early Detection, Critical Diseases, Machine Learning Algorithms. | | --- | --- | INTRODUCTION: Within the dynamic realm of healthcare, the timely identification of pivotal diseases like Cancer, Cardiovascular Diseases, Diabetes, Alzheimer's, and Chronic Kidney Disease (CKD) serves as a foundational pillar, aiming to enhance patient outcomes and alleviate the strain on healthcare systems. This imperative focus on early detection underscores the significance of proactive measures in addressing critical health conditions, paving the way for more effective treatments and ultimately contributing to a more sustainable and responsive healthcare landscape. Timely identification of diseases at their nascent stages not only facilitates more effective treatment but also holds the potential to enhance the overall quality of life for affected individuals. In this context, the integration of machine learning (ML) algorithms has emerged as a promising avenue, leveraging the power of computational intelligence to sift through vast and complex datasets for subtle patterns indicative of impending health challenges. This research paper undertakes a comprehensive review and analysis of diverse ML algorithms employed in the critical domain of early disease detection. By scrutinizing the strengths, limitations, and performance metrics of these algorithms, this study aims to provide a nuanced understanding of their applicability and efficacy in the pursuit of timely and accurate identification of critical diseases. As we embark on this exploration, we seek not only to elucidate the current landscape but also to pave the way for future advancements in leveraging machine learning for the early detection of diseases that demand swift intervention. LITERATURE REVIEW: There have been numerous studies done related to predicting the disease using different machine learning techniques and algorithms which can be used by medical institutions. This paper reviews some of those studies done in research papers using the techniques and results used by them. Chetty et al [1] proposed a system utiliz

FAQs

Which machine learning algorithms are reviewed for early detection of critical diseases?

The review covers Logistic Regression, Support Vector Machines, Random Forests, Neural Networks, K-Nearest Neighbors, and Ensemble Learning.

What criteria does the paper use to assess algorithm suitability for early detection?

Algorithms are assessed based on interpretability, scalability, and performance handling diverse data types.

How does the literature review connect prior work to early disease detection?

It summarizes studies using different ML techniques for disease prediction, highlighting reported accuracies and relative strengths of specific approaches across diseases like diabetes, liver disorders, breast cancer, and chronic kidney disease.

Aug 4, 2026

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