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Optimized Adaptive Neuro-Fuzzy Inference Model for Early Identification of Premature Heart Attacks
Subject area: Science,Engineering and Technology · Area of research: Computer Science
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
How to cite this paper
@article{1713801,
author = {S. E. Dogo, E. J. Garba, Y. M. Malgwi, A. Danlami},
title = {Optimized Adaptive Neuro-Fuzzy Inference Model for Early Identification of Premature Heart Attacks},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {7},
pages = {1621-1631},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1713801.pdf},
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},
month = {January},
}