Embedded Gene Expression Data Based On RidgeClassifier For Alzheimer’s Disease Classification

In bioinformatics, analyzing large-scale gene expression data using machine learning models is a common practice. However, selecting relevant features from high-dimensional data poses a significant challenge. Feature selection techniques aim to address this challenge by reducing data size, selecting important features, and reducing classification time. While filter selection is a fast approach, the selected features may lack biological relevance. Wrapper techniques have shown efficacy in selecting relevant features, but they can be computationally intensive and time-consuming. Embedded techniques bridge this gap by selecting relevant features while maintaining computational efficiency and low cost. This study introduces a framework that utilizes the embedded feature selection based on the RidgeClassifier technique for selecting gene expression data (EGSRC) from the ADNI dataset. Hyperparameter tuning using Optuna is also employed to further improve the performance of the SVM classifier in classification learning. The study evaluates the performance of five different classifiers using various evaluation metrics through cross-validation. SVM achieved the highest scores across all metrics, recording 99.47% accuracy, 99.25% precision, 98.41% specificity, 100% recall, 99.62% F1-score, and 99.20% AUC. The proposed framework’s RC selector outperforms filter, embedded techniques, and state-of-the-art techniques, which demonstrates its robustness.