Predicting Multiple Diseases Using Machine Learning: A Data-Driven Approach

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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 : 2025 | Volume : 16 | Issue : 02 | Page : –
    By

    Anamika Rana,

  • Sushma Malik,

  • Kanika,

  • Ashima,

  1. Associate Professor, Department of Computer Applications, Maharaja Surajmal Institute, Janakpuri, Delhi, India
  2. Assistant Professor, Professor, Department of Computer Applications, Maharaja Surajmal Institute, Janakpuri, Delhi, India
  3. Scholar, Department of Computer Applications, Maharaja Surajmal Institute, Janakpuri, Delhi, India
  4. Scholar, Department of Computer Applications, Maharaja Surajmal Institute, Janakpuri, Delhi, India

Abstract

The increasing prevalence of chronic and life-threatening diseases highlights the need for innovative healthcare solutions that enable early detection and proactive management. The Multiple Disease Prediction Platform is a web-based system utilizing Machine Learning (ML) and Deep Learning (DL) algorithms to analyze user-inputted health data, generating real-time predictions of potential health risks. By leveraging Python’s Stream lit library, the platform provides an interactive and accessible diagnostic experience, eliminating the need for frequent clinical visits and enhancing remote healthcare accessibility. This research focuses on developing a supervised learning-based system trained on credible datasets (e.g., Kaggle) for disease prediction. Exploratory and descriptive research methods were employed, incorporating statistical analysis and data visualization to enhance accuracy. Despite its potential, the platform faces limitations, including data privacy concerns, regional biases, reliance on incomplete user data, and computational demands affecting cost and accessibility in resource- limited settings. The study has significant practical implications in advancing digital healthcare, enabling early disease risk assessment and improving decision-making for individuals and healthcare professionals. Integrating bio-inspired algorithms further enhances predictive performance by optimizing model efficiency. While deep learning improves complex pattern recognition, ongoing research is essential to refine these models for greater accuracy, scalability, and reliability. Addressing ethical AI challenges, model biases, and computational efficiency is crucial for ensuring broad applicability and long-term sustainability.

Keywords: Machine learning, deep learning, disease prediction, digital healthcare, bio-inspired algorithms, supervised learning, data privacy, predictive analytics, health informatics, remote healthcare

[This article belongs to Journal of Computer Technology & Applications ]

How to cite this article:
Anamika Rana, Sushma Malik, Kanika, Ashima. Predicting Multiple Diseases Using Machine Learning: A Data-Driven Approach. Journal of Computer Technology & Applications. 2025; 16(02):-.
How to cite this URL:
Anamika Rana, Sushma Malik, Kanika, Ashima. Predicting Multiple Diseases Using Machine Learning: A Data-Driven Approach. Journal of Computer Technology & Applications. 2025; 16(02):-. Available from: https://journals.stmjournals.com/jocta/article=2025/view=208004



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Regular Issue Subscription Review Article
Volume 16
Issue 02
Received 01/03/2025
Accepted 08/03/2025
Published 15/04/2025
Publication Time 45 Days
Total Visits: 33


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