Machine Learning in Healthcare: A Comparative Review of Techniques and Applications

Abstract

The rise of machine learning has profoundly impacted healthcare, enhancing the interpretation and utilization of medical data. It emphasizes how machine learning may improve diagnosis accuracy, maximize treatment choices, and advance precision medicine.According to previousresearch, machine learning algorithms are highly accurate in disease diagnosis.But comprehensive information on algorithms accuracy is rarely available in a single study, making access time-consuming. So, the objective of this work is to provide necessary information about these algorithms used in healthcare and to reviewvarious applications of machine learning in healthcareincluding disease diagnosis, personalized medicine, medical imaging and patient monitoring.A comparative analysis of these approaches is conducted based on accuracy, across multiple healthcare applications including Breast Cancer, Heart Disease, Diabetes, COVID-19 and Glaucoma predictionfrom the literature and highlighting best algorithm for specific disease.The growing uses of machine learning in healthcare are examined in this study, which offers important insights for creating more intelligent and responsive machine learning solutions that enhance patient outcomes and accelerate medical research. Future directions include advanced machine learning models, multi-modal data integration, personalized healthcare, and real-world deployment challenges.

Introduction

This paper reviews the application of machine learning (ML) in healthcare, particularly its role in disease diagnosis, prediction, and personalized treatment. Machine learning techniques, including traditional methods (e.g., Decision Trees, SVM) and advanced deep learning models (e.g., CNNs, RNNs), have significantly improved accuracy and efficiency in diagnosing diseases such as breast cancer, heart disease, diabetes, COVID-19, and glaucoma.

The study systematically selected and analyzed research papers from 2018 to 2025, narrowing down to 25 highly relevant studies for comparative analysis. It highlights that deep learning and ensemble methods often outperform traditional algorithms in medical imaging, risk assessment, and patient monitoring.

Key healthcare applications of ML include early disease detection, prognosis, personalized medicine, medical imaging diagnostics, patient monitoring, and predictive analytics. Tables in the paper summarize the performance of various ML models across different diseases and datasets, demonstrating high accuracy levels (often above 90%) for state-of-the-art approaches.

Conclusion

Machine learning in healthcare has evolved significantly from early diagnostic models toadvanced hybrid and deep learning techniques. Every disease has to be recognizedin earlier stage in healthcare domain. The keyfindings of this paper emphasize the important role of preprocessing techniques, data quality and feature selection in enhancing model performance. The study explores various machine learning algorithms detailing their applications in disease diagnosisand prediction. In this studythe review of various machine learning algorithms for the healthcare is conducted. This paper also includes various types of diseasesand different diseases datasets are compared based on their corresponding accuracy.Future advancements will focus on data availability, model interpretability, personalizedmedicine, and ethical artificial intelligence integration to revolutionize healthcare decision-making. The paper suggests future directions for overcoming these challenges, such as developing more interpretable models, implementing secure data-sharing frameworks like federated learning.

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Copyright © 2025 Harshita Sharma, Prof. K. L. Bansal. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.