Proceedings of the International Conference on Intelligent Systems for a Sustainable Future (ISSF 2026)

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Multi-Disease Risk Prediction Engine

Authors
S. Md. Sami1, S. Bhavana1, V. R. Niveditha2, *
1Undergraduate Student, Department of CSE, Sathyabama Institute of Science and Technology, Chennai, India
2Assistant Professor, Department of CSE, Sathyabama Institute of Science and Technology, Chennai, India
*Corresponding author. Email: niveditha.cse@sathyabama.ac.in
Corresponding Author
V. R. Niveditha
Available Online 16 June 2026.
DOI
10.2991/978-94-6239-693-7_24How to use a DOI?
Keywords
Multi-disease prediction; deep learning; Flask API; InceptionV3; VGG19; medical imaging; early detection; Transfer learning; disease diagnosis
Abstract

The combination of data science and technology has revolutionized disease detection and prediction in today’s healthcare environment. This research presents an advanced approach of multi-disease prediction using deep learning architectures, namely InceptionV3 and VGG19 in the framework of a Flask-based API. The targeted diseases are kidney stone, kidney cyst, tumor in kidney, adenocarcinoma, large cell carcinoma and brain tumor. The methodology includes the extensive data pre-processing such as resizing, normalizing and augmentation for increasing diversity in the dataset. The patterns learned in the pre-trained models are quite intricate and can be better predicted-after being retrained with the medical imaging data. The system gives real-time predictions of disease according to the user uploaded medical image with user-friendly interface. The implementation uses Flask as the backend processing, it integrates the prediction models seamlessly to provide the result, which aims to facilitate early detection of disease and enhance the healthcare results.

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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Volume Title
Proceedings of the International Conference on Intelligent Systems for a Sustainable Future (ISSF 2026)
Series
Atlantis Highlights in Intelligent Systems
Publication Date
16 June 2026
ISBN
978-94-6239-693-7
ISSN
2589-4919
DOI
10.2991/978-94-6239-693-7_24How to use a DOI?
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.

Cite this article

risenwbib
TY  - CONF
AU  - S. Md. Sami
AU  - S. Bhavana
AU  - V. R. Niveditha
PY  - 2026
DA  - 2026/06/16
TI  - Multi-Disease Risk Prediction Engine
BT  - Proceedings of the International Conference on Intelligent Systems for a Sustainable Future (ISSF 2026)
PB  - Atlantis Press
SP  - 230
EP  - 242
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6239-693-7_24
DO  - 10.2991/978-94-6239-693-7_24
ID  - Sami2026
ER  -