International Journal of Computational and Electronic Aspects in Engineering
Volume 7 · Issue 2 · June 2026 · pp. 305-315
Research Article · Peer Reviewed
Received: April 15, 2026 · Accepted: June 23, 2026 · Published: June 28, 2026
DOI: 10.26706/ijceae.7.2.20260318
Open Access · CC BY 4.0

ANFIS-Based Intelligent Framework for Predicting Health Risk Levels Associated with Diabetes and Blood Pressure Abnormalities

Lubab H. Albak*
Department of Computer Techniques Engineering, College of Technical Engineering for Computer and AI, Northern Technical University, Mosul 41000, Iraq.

*Corresponding author: Lubab_harith@ntu.edu.iq

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Abstract

Diabetes mellitus and high blood pressure are two of the most common chronic diseases worldwide and are associated with severe cardiovascular and metabolic complications. Early prediction of these conditions is essential for timely diagnosis, treatment, and long-term health management. Intelligent systems capable of modeling complex nonlinear relationships between physiological parameters have become increasingly important in medical decision support systems. This study presents an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based intelligent framework for predicting health risk levels associated with diabetes and blood pressure abnormalities. Three physiological input parameters, namely blood glucose level, systolic blood pressure, and diastolic blood pressure, were utilized to estimate the overall health risk. Various input and output membership functions were investigated using MATLAB simulations. A hybrid learning algorithm combining least-squares estimation and backpropagation was employed to optimize the ANFIS model. The experimental results demonstrated that the best prediction performance was achieved using a linear output membership function together with product sigmoidal input membership functions. After 30 training epochs, the model achieved training and testing errors of 0.0087517 and 0.0087618, respectively, indicating stable learning behavior and excellent generalization capability without overfitting. The proposed ANFIS-based framework efficiently predicts combined diabetes and hypertension risk and provides a reliable intelligent decision-support tool for early diagnosis of chronic diseases.

Keywords

Adaptive Neuro-Fuzzy Inference System (ANFIS) Diabetes Mellitus Blood Pressure Health Risk Prediction Fuzzy Logic Artificial Intelligence Medical Decision Support

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