Optimized Adaptive Neuro-Fuzzy Inference Model for Early Identification of Premature Heart Attacks

• Author(s): S. E. Dogo; E. J. Garba; Y. M. Malgwi; A. Danlami
• Paper ID: 1713801
• Page: 1621-1631
• Published Date: 22-01-2026
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 7 January-2026

Abstract

Cardiovascular diseases (CVDs), particularly premature heart attacks, remain a leading cause of mortality worldwide, with increasing prevalence in developing countries such as Nigeria. Early detection is critical for effective prevention and clinical intervention. This study proposes an Ensemble Adaptive Neuro-Fuzzy Inference System (ANFIS) for the early diagnosis of premature heart attacks using clinically relevant patient data. The dataset was obtained from the Federal Medical Centre (FMC), Jalingo, Taraba State, and supplemented with benchmark datasets, comprising 918 patient records with 12 attributes related to cardiovascular health. Data preprocessing, feature encoding, and model training were conducted using Python-based machine learning and fuzzy logic libraries. The proposed ANFIS model was trained and evaluated using standard performance metrics. Experimental results demonstrate that the model achieved an overall accuracy of 92%, with high precision, recall, and F1-scores across both classes. The findings indicate that the ensemble ANFIS model is effective, robust, and suitable as an intelligent decision-support tool for early detection of premature heart attacks.

Keywords

Fuzzy Logic, Adaptive Neuro-Fuzzy Inference System, Cardiovascular Diseases

Citations

IRE Journals:
S. E. Dogo, E. J. Garba, Y. M. Malgwi, A. Danlami "Optimized Adaptive Neuro-Fuzzy Inference Model for Early Identification of Premature Heart Attacks" Iconic Research And Engineering Journals Volume 9 Issue 7 2026 Page 1621-1631

IEEE:
S. E. Dogo, E. J. Garba, Y. M. Malgwi, A. Danlami "Optimized Adaptive Neuro-Fuzzy Inference Model for Early Identification of Premature Heart Attacks" Iconic Research And Engineering Journals, 9(7)