TITLE:
Contribution of the Chebyshev Polynomial to Automatic Diagnosis of Alzheimer’s Disease Based on Machine Learning
AUTHORS:
Stephanie-Claude Pelap Ngassa, Edith Belise Kenmogne, Clementin Tayou Djamegni
KEYWORDS:
Discrete Chebyshev Transform (DChT), EEG Signal, Dimension Reduction, Classification, Feature Extraction
JOURNAL NAME:
Advances in Artificial Intelligence and Robotics Research,
Vol.2 No.3,
September
16,
2026
ABSTRACT: Alzheimer’s disease (AD) is a neurodegenerative disorder affecting brain lobe cells. Several techniques have been developed to diagnose AD. This study proposes a new technique for the automatic diagnosis of AD based on the analysis of EEG signals. This involves preprocessing, spectral analysis based on the Discrete Chebyshev Transform (DChT), extraction of 85 features, dimensionality reduction, and classification. Classification is performed using k-nearest neighbors, support vector machines, XGBoosting, AdaBoosting, GradientBoosting, Random Forest and Multi-Layer Perceptron. In addition, the classification accuracies obtained without the proposed model were examined. Furthermore, Boruta’s feature selection algorithm and the Mann-Whitney test confirm the benefits of using the proposed model, especially DChT as a spectral analysis tool. The impact of training set size on classification accuracy was analyzed. For classification using dimensionality reduction, the best accuracy is 88.64% and was achieved by GradientBoosting. Without dimensionality reduction, the best accuracy is 93.24% and was achieved by XGBoost. These performances are as high as those of other methods proposed in the literature. Therefore, DChT is an effective tool for improving AD diagnosis. However, using DChT is beneficial for classification and the results are the best; this work found a new technique for diagnosing AD using artificial intelligence.