[email protected] +91 9840521421 Scholarly Open Access, Peer-reviewed, Refereed Journal e-ISSN 2582-8746 Since 2020
  1. Home
  2. Archives
  3. 2024
  4. Vol. 5 Issue 3
  5. Article
Research Article

Multi Disease Prediction System Using Machine Learning

  • Paper ID 163
  • Volume 5, Issue 3
  • Pages 112-115
  • Published Online: May-June 2024
  • 2 Views
Open Access Peer Reviewed

Authors

  • Niyati Gaur1
  • Simran Fatima2
  • Raghvendra Pratap SIngh3
  • Himanshu Patel4

1Assistant Professor, Computer Science and Engineering, School of Management Sciences, Lucknow, Uttar Pradesh, India. 234 Final Year Students, Computer Science and Engineering, School of Management Sciences, Lucknow, Uttar Pradesh, India.

Article Information

DOI
No DOI
Published Online
May-June 2024
Pages
112-115
Article Type
Research Article
Check for updates
No DOI available for cross-reference

Abstract

View PDF

Abstract: The Multi Disease Prediction System (MDPS) utilizes machine learning techniques, particularly Logistic Regression and Support Vector Machines (SVM), to forecast various illnesses including diabetes, heart disease, and Parkinson's disease. It employs a user-friendly interface developed with Streamlit, enhancing accessibility. By analysing standard health indicators such as blood pressure, pulse rate, cholesterol, and heart rate, the system aims to improve early detection and enable personalized healthcare. The model demonstrates notable accuracy and precision, aiding in the identification of critical risk factors associated with these ailments. This paper highlights the essential role of machine learning in simultaneously predicting multiple diseases, addressing a current gap in technological capabilities. The project underscores the advancement of disease prediction through sophisticated machine learning methodologies.

APC from ₹1200 + 18% GST

Peer-reviewed open access₹1200 Indian (₹1400 with DOI)$80 USD internationalpaid after acceptance Peer-reviewed open access₹1200 Indian$80 USD international ₹1200 Indian · $80 intl

  • CrossRef DOI
  • ISSN 2582-8746
  • Open Access
  • Peer Reviewed
  • Plagiarism Checked
  • Google Scholar
Submit Paper