A Multi-Disease Prediction System for Diabetic Complications Using Machine Learning and Deep Learning

DOI

10.2991/978-94-6239-693-7_26How to use a DOI?

Keywords

Diabetes mellitus; ill complications; multi-disease prediction; machine learning; intense learning; Random Forest; convolutional neuronic network; risk assessment; non-subjective decision support; healthcare analytics

Abstract

Diabetes mellitus is a problem that affects many parts of the body and gets worse over time. near of the ways we try to predict what will happen to people with Diabetes mellitus only look at one thing at a time. This study is trying to do something. It is trying to make a system that can predict all the complications of Diabetes mellitus. The system uses things like blood sugar levels, heart health, kidney health and how extendable someone has had Diabetes mellitus to make predictions. It uses these things to predict if someone will get nerve damage kidney damage or heart problems. The system uses a kind of computer program called Random Forest models to make these predictions. It also uses a kind of computer program called a convolutional neuronic network to look at pictures of the eyes and predict if someone will obtain eye damage from Diabetes mellitus. All of these predictions are then combined into one score that shows the risk of complications from Diabetes mellitus. This score can help doctors understand what might happen to people, with Diabetes mellitus. The system is deployed as a web based application, enabling real time, extensive risk assessment to support proactive clinical decision making.

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  - Paduchuri Jayasree
AU  - Chengamma Chitteti
AU  - K. Reddy Madhavi
AU  - Bhumireddy Raviteja
AU  - E. Rohith
AU  - Galla Jayadev
PY  - 2026
DA  - 2026/06/16
TI  - A Multi-Disease Prediction System for Diabetic Complications Using Machine Learning and Deep Learning
BT  - Proceedings of the International Conference on Intelligent Systems for a Sustainable Future (ISSF 2026)
PB  - Atlantis Press
SP  - 250
EP  - 257
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6239-693-7_26
DO  - 10.2991/978-94-6239-693-7_26
ID  - Jayasree2026
ER  -