The labor-intensive and subjective nature of Wireless Capsule Endoscopy (WCE) limits its use for diagnosing gastrointestinal disorders as a non-invasive diagnostic technique. CAD systems supplement this innovation by providing healthcare professionals with the information they need to make more informed decisions about disease identification. Through the integration of WCE and CADS, gastrointestinal diagnostic capability is significantly enhanced. Our proposed Vision Transformer-based feature extraction framework coupled with various Machine Learning (ML) classifiers for automated WCE image classification further enhances this synergy. By exploring alternative classifier options, we aim to improve the results of Vision Transformer, which typically uses a Multi-Layer Perceptron (MLP) as a classifier. In this paper, we compare traditional ML classifiers and the standard MLP with respect to WCE image classification. ViT extracts rich spatial and contextual features, which are then fed into ML algorithms including Support Vector Machine (SVM), Random Forest (RF), CatBoost, Logistic Regression, K-Nearest Neighbors (KNN), LightGBM, and XGBoost. We evaluate each classifier independently for accuracy, precision, recall, F1-score, and specificity by using standard performance metrics. Our experimental results demonstrate that CatBoost outperforms all other classifiers, including the conventional MLP, achieving superior classification accuracy and robustness.