Neural network-based disease prediction: Leveraging symptoms for accurate diagnosis of multiple diseases

Said Badreddine (), Tariq Alwadan (), Asem Omari (), Hamsa Al Ammari (), Rashid Ashraf () and Rachid Moustaquim ()

Edelweiss Applied Science and Technology, 2025, vol. 9, issue 5, 1932-1941

Abstract: The development of technology and the availability of patient data have been increasing the leveraging of a data-driven approach to improve diagnostic accuracy. This research introduces a virtual diagnosis program that employs neural networks to predict diseases based on a dataset of 4,920 patients and 132 symptoms. Through exploratory data analysis and correlation analysis, significant associations between symptoms and diseases are identified. The developed system achieves an impressive accuracy rate of 95.6% in diagnosing diseases by utilizing advanced optimization techniques for training the neural network model. This accuracy demonstrates the potential of the program to assist healthcare professionals in making accurate diagnoses, enhancing the precision and efficiency of disease identification. The data-driven approach of this virtual diagnosis tool complements medical expertise, offering valuable support for timely and accurate diagnoses.

Keywords: Disease prediction; Healthcare technology; Machine learning; Neural networks. (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://learning-gate.com/index.php/2576-8484/article/view/7349/2538 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ajp:edwast:v:9:y:2025:i:5:p:1932-1941:id:7349

Access Statistics for this article

More articles in Edelweiss Applied Science and Technology from Learning Gate
Bibliographic data for series maintained by Melissa Fernandes ().