The Early Brain Hemorrhage Prediction System Using Machine Learning

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2026 | Volume : 16 | Issue : 02 | Page :
    By

    Rohan Srivastava,

  • Swapnil Srivastava,

  • Ram Bhushan,

  • Archana Dwivedi,

  1. Student, Computer Science & Engineering, Babu Banarasi Das Institute of Technology and Management, Lucknow, Uttar Pradesh, India
  2. Student, Computer Science & Engineering, Babu Banarasi Das Institute of Technology and Management, Lucknow, Uttar Pradesh, India
  3. Associate Professor, Department of Computer Science & Engineering, Babu Banarasi Das Institute of Technology and Management, Lucknow, Uttar Pradesh, India
  4. Assistant Professor, Department of Computer Science & Engineering, Babu Banarasi Das Institute of Technology and Management, Lucknow, Uttar Pradesh, India

Abstract

Brain hemorrhage is a critical medical emergency that requires immediate attention, as delays in diagnosis can result in severe neurological damage or death. The condition involves bleeding within or around brain tissues, leading to increased intracranial pressure and disruption of normal brain function. Although imaging techniques such as CT scans and MRI provide accurate diagnosis, their availability is limited in emergency and rural settings. In recent years, machine learning has emerged as a promising solution for early prediction using structured clinical data. This study provides a detailed review of various machine learning approaches, including traditional models, ensemble techniques, and deep learning methods. The analysis shows that ensemble models like Random Forest and XGBoost achieve strong performance with clinical data, while deep learning models excel in image-based detection. However, challenges such as lack of real-time deployment, limited interpretability, and dependency on large datasets still exist, highlighting the need for practical and scalable solutions.

Keywords: Brain Hemorrhage, Machine Learning, Random Forest, XGBoost, Deep Learning, Medical Imaging

[This article belongs to Research and Reviews: A Journal of Neuroscience ]

How to cite this article:
Rohan Srivastava, Swapnil Srivastava, Ram Bhushan, Archana Dwivedi. The Early Brain Hemorrhage Prediction System Using Machine Learning. Research and Reviews: A Journal of Neuroscience. 2026; 16(02):-.
How to cite this URL:
Rohan Srivastava, Swapnil Srivastava, Ram Bhushan, Archana Dwivedi. The Early Brain Hemorrhage Prediction System Using Machine Learning. Research and Reviews: A Journal of Neuroscience. 2026; 16(02):-. Available from: https://journals.stmjournals.com/rrjons/article=2026/view=242841


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Regular Issue Subscription Review Article
Volume 16
Issue 02
Received 13/04/2026
Accepted 25/04/2026
Published 04/05/2026
Publication Time 21 Days
17

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