Machine Learning-Based Disease Prediction: A Comparative Analysis for Diabetes, Brain Tumor, and Parkinson’s Disease

Year : 2025 | Volume : 15 | Issue : 02 | Page : 44 54
    By

    Abhas Nigam,

  • Akshat Pandey,

  • Govind Kumar Singh,

  • Keshav Kumar Tiwari,

  1. Student, Department of Artificial Intelligence & Machine Learning, Galgotias College of Engineering & Technology, Greater Noida, Uttar Pradesh, India
  2. Student, Department of Artificial Intelligence & Machine Learning, Galgotias College of Engineering & Technology, Greater Noida, Uttar Pradesh, India
  3. Student, Department of Artificial Intelligence & Machine Learning, Galgotias College of Engineering & Technology, Greater Noida, Uttar Pradesh, India
  4. Assistant. Professor, Department of Computer Science & Engineering, Galgotias College of Engineering & Technology, Greater Noida, Uttar Pradesh, India

Abstract

This paper presents a web-based disease prediction system that integrates machine learning and deep learning techniques to assist in the early detection of Parkinson’s Disease, Diabetes, and Brain Tumors. By utilizing clinical data and MRI images, the platform provides rapid and interpretable predictions to support proactive health management. Logistic Regression models are applied to classify structured datasets for predicting Parkinson’s disease and Diabetes, making use of their effectiveness in binary classification tasks. In contrast, a Convolutional Neural Network (CNN) is employed to identify brain tumors from MRI scans, leveraging its ability to extract spatial features from imaging data. This combination of traditional machine learning for tabular data and deep learning for image analysis provides an effective and accurate approach for diagnosing various medical conditions using domain- specific data types. The system is developed with a Flask-based backend and a user-friendly web interface, allowing seamless interaction for users to input data or upload images and receive real-time results. This work emphasizes the potential of AI-powered tools in the healthcare sector, particularly in facilitating early screening and promoting awareness. Additionally, it establishes a foundation for future advancements, including expanding the system to cover a wider range of diseases and integrating it more deeply with clinical workflows for broader medical application and impact.

Keywords: Disease prediction, parkinson’s disease, diabetes, brain tumor, logistic regression, convolutional neural network, flask, medical imaging, machine learning, deep learning, healthcare AI

[This article belongs to Current Trends in Signal Processing ]

How to cite this article:
Abhas Nigam, Akshat Pandey, Govind Kumar Singh, Keshav Kumar Tiwari. Machine Learning-Based Disease Prediction: A Comparative Analysis for Diabetes, Brain Tumor, and Parkinson’s Disease. Current Trends in Signal Processing. 2025; 15(02):44-54.
How to cite this URL:
Abhas Nigam, Akshat Pandey, Govind Kumar Singh, Keshav Kumar Tiwari. Machine Learning-Based Disease Prediction: A Comparative Analysis for Diabetes, Brain Tumor, and Parkinson’s Disease. Current Trends in Signal Processing. 2025; 15(02):44-54. Available from: https://journals.stmjournals.com/ctsp/article=2025/view=213781


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Regular Issue Subscription Original Research
Volume 15
Issue 02
Received 15/05/2025
Accepted 16/05/2025
Published 21/06/2025
Publication Time 37 Days
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