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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Sabiri, Issa
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Computer Science & IT Engineering

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Epilepsy detection using wavelet transform, genetic algorithm, and decision tree classifier Zougagh, Lahcen; Bouyghf, Hamid; Nahid, Mohammed; Sabiri, Issa
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp3447-3455

Abstract

This work presents a unique detection approach for classifying epilepsy using the CHB_MIT dataset. The suggested system utilizes the discrete wavelet transform (DWT) technique, genetic algorithm (GA), and decision tree (DT). This model consists of three distinct steps. In the first one, we present a feature extraction method that uses a DWT of four levels on electroencephalogram (EEG) and electrocardiogram (ECG) signals. The second step is the process of feature selection, which entails the elimination of irrelevant features in order to produce datasets of superior quality. This is achieved via the use of correlation and GA techniques. The reduction in dimensionality of the dataset serves to decrease the complexity of the training process and effectively addresses the problem of overfitting. The third step utilizes a DT algorithm to make predictions based on the data of epileptic patients. The performance evaluation layer encompasses the implementation of our prediction model on the CHB-MIT dataset. The results achieved from this implementation show that using feature selection techniques and an ECG signal as additional information increases the detection model's performance. The averaging accuracy is 98.3%, the sensitivity is 96%, and the specificity is 99%.
Heart disease prediction optimization using metaheuristic algorithms Nouna, Zaid; Bouyghf, Hamid; Nahid, Mohammed; Sabiri, Issa
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp4332-4341

Abstract

This study explores metaheuristics hyperparameter tuning effectiveness in machine learning models for heart disease prediction. The optimized models are k-nearest neighbors (KNN) and support vector machines (SVM) using metaheuristics to identify configurations that minimize prediction error. Even though the main focus is utilizing metaheuristics to efficiently navigate the hyperparameter search space and determine optimal setting, a pre-processing and feature selection phase precedes the training phase to ensure data quality. Convergence curves and boxplots visualize the optimization process and the impact of tuning on model performance using three different metaheuristics, where an error of 0.1188 is reached. This research contributes to the field by demonstrating the potential of metaheuristics for improving heart disease prediction performance through optimized machine learning models.
Co-Authors Bouyghf, Hamid Nahid, Mohammed Nouna, Zaid Zougagh, Lahcen