DiagniQ: An AI-Based Multi-Disease Prediction System Using Hybrid Machine Learning and Deep Learning Models

DOI

10.2991/978-94-6239-697-5_20How to use a DOI?

Keywords

Convolutional Neural Networks (CNN); Residual Neural Networks (ResNet); Support Vector Machines (SVM) and Decision Tree classifiers

Abstract

The timely diagnosis of chronic and life-threatening diseases is crucial to enhancing patient outcomes and lowering the rate of burden in the healthcare systems. The paper introduces DiagniQ, which is an artificial intelligence (AI) multi-disease prediction algorithm that helps to identify breast cancer, lung cancer, diabetes and heart disease at the early stages. The proposed system combines both machine learning and deep learning to work with structured clinical data as well as medical images. Image-based disease prediction uses Convolutional Neural Networks (CNN) and Residual Neural Networks (ResNet), whereas structured datasets are analyzed with the help of Support Vector Machines (SVM) and Decision Tree classifiers. The suggested system is a multi-disease prediction system that is integrated and unified. DiagniQ accepts two types of input channels that can be entered in by a user manually or by submitting a structured medical report. Experimental analysis shows the potential of DiagniQ though with all diseases which means that DiagniQ can be a useful early check and decision-support system that facilitates preventive health and medical timely consultation.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Saara Goyal
AU  - Aastha Maheshwari
AU  - Varun Srivastava
AU  - Rajiv Kumar Mishra
PY  - 2026
DA  - 2026/06/04
TI  - DiagniQ: An AI-Based Multi-Disease Prediction System Using Hybrid Machine Learning and Deep Learning Models
BT  - Proceedings of the Conference on Bridging Engineering Disciplines with AI and Machine Learning (BEDAIML 2026)
PB  - Atlantis Press
SP  - 233
EP  - 241
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-697-5_20
DO  - 10.2991/978-94-6239-697-5_20
ID  - Goyal2026
ER  -