Improving The Accuracy of Medical Diagonosis Detection Using Machine Learning

Year : 2025 | Volume : 12 | Issue : 03 | Page : 1 8
    By

    G Shivaji Rao,

  • Manoj Kumar S,

  • Ranjith Prasath M V,

  • Santhosh S,

  1. Researcher, Department of Computer Science and Design, Karpagam College of Engineering Coimbatore, Tamil Nadu, India
  2. Researcher, Department of Computer Science and Design, Karpagam College of Engineering Coimbatore, Tamil Nadu, India
  3. Researcher, Department of Computer Science and Design, Karpagam College of Engineering Coimbatore, Tamil Nadu, India
  4. Researcher, Department of Computer Science and Design, Karpagam College of Engineering Coimbatore, Tamil Nadu, India

Abstract

While accurate and timely medical diagnosis is a fundamental aspect of effective health care delivery, traditional methods have not been able to overcome major hurdles such as inefficiencies in data analysis with Gi Human Error as well as limitations in scalability. The “Improved Accuracy of Medical Diagnosis Detection Using Machine Learning” project seamlessly integrates advanced machine learning (M L) technologies with efficient preprocessing and feature selection techniques to outperform all above-mentioned hurdles. The proposed system utilizes Python-based tools for preprocessing medical datasets, eliminating inconsistencies, and relevant features to enable the best performance of those models. Machine learning algorithms, like SVM, were implemented to classify and find patterns in a specific piece of medical data, allowing the correct diagnosis of such chronic and complicated cases. Modular in architecture, the backing of a system provides a streamlined flow from preprocessing-to healthcare application. Performance evaluation of the system on benchmark datasets indicated that disease detection accuracy is high enough to validate the way it can improve traditional methods of diagnosis. Adaptability and scalability make this framework ready for many medical domains, alongside suitable healthcare providers to make data-driven decisions and hence improve patient outcomes. This project provides an important step toward applying machine learning to significantly transform healthcare into a more efficient, accurate, and personalized source of solutions to such issues.

Keywords: Machine Learning, Medical Diagnosis, Data Preprocessing, Disease Detection, Healthcare Analytics.

[This article belongs to Research & Reviews: A Journal of Bioinformatics ]

How to cite this article:
G Shivaji Rao, Manoj Kumar S, Ranjith Prasath M V, Santhosh S. Improving The Accuracy of Medical Diagonosis Detection Using Machine Learning. Research & Reviews: A Journal of Bioinformatics. 2025; 12(03):1-8.
How to cite this URL:
G Shivaji Rao, Manoj Kumar S, Ranjith Prasath M V, Santhosh S. Improving The Accuracy of Medical Diagonosis Detection Using Machine Learning. Research & Reviews: A Journal of Bioinformatics. 2025; 12(03):1-8. Available from: https://journals.stmjournals.com/rrjobi/article=2025/view=230764


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Regular Issue Subscription Review Article
Volume 12
Issue 03
Received 26/03/2025
Accepted 01/09/2025
Published 08/11/2025
Publication Time 227 Days
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