Archives
International Journal of Zoology and Applied Biosciences Research Article
Chronic diseases prediction using fuzzy logic and machine learning with data preprocessing handling
J Greeda, V. Vinoba and S. Indrakala
Year : 2026 | Volume: 11 | Issue: 3 | Pages: 329-337
Received on: 13/04/2026
Revised on: 27/04/2026
Accepted on: 22/05/2026
Published on: 15/06/2026
-
J Greeda, V. Vinoba and S. Indrakala( 2026).
Chronic diseases prediction using fuzzy logic and machine learning with data preprocessing handling
. International Journal of Zoology and Applied Biosciences, 11( 3), 329-337.
-
click to view the cite format
Abstract
Chronic diseases like diabetes, heart problems, and cancer are major global health concerns, making early detection crucial. Predicting these conditions using machine learning can save lives by reducing prediction errors and improving reliability. This review explores various machine learning techniques, including supervised learning and deep learning, and highlights the importance of data quality and model selection in achieving high predictive performance. The review examines data preprocessing methods like handling missing values, outlier detection, and feature selection, which play a vital role in improving prediction accuracy. The findings emphasize that good data and choosing the right model are key to making accurate predictions. By improving preprocessing strategies and machine learning techniques, we can enhance chronic disease prediction and ultimately improve public health outcomes. This review provides insights into the current state of machine learning in chronic disease prediction, highlighting challenges and future opportunities for improvement. With the growing burden of chronic diseases, accurate prediction models can make a significant difference in healthcare.
Keywords
Chronic Disease Prediction, Fuzzy Logic, Machine Learning, Data Preprocessing, Supervised Learning.
-
Full Article PDF (
1)
- View HTML Article
Copy Rights
© The Author(s) 2025. This article is published by International Journal of Zoology and Applied Biosciences under the terms of the Creative Commons Attribution 4.0 International License (creativecommons.org), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
