the increasing volume of medical data presents both opportunities and challenges for enhancing classification
methods. While Support Vector Machines (SVMs) are widely recognized for their effectiveness in various classification tasks,
traditional SVM approaches exhibit significant limitations when applied to large-scale datasets. This paper presents a hybrid
feature selection framework designed to enhance the classification performance of medical data by identifying the most relevant
features for Support Vector Machine (SVM) classifiers. The proposed framework consists of two phases. The first phase
combines Information Gain with several optimization techniques, including Particle Swarm Optimization (PSO), Bat Search
Method, Elephant Search Algorithm (ESA), and Firefly Algorithm, to effectively select key features for medical datasets. Our
results show that the hybrid framework significantly improves classification accuracy compared to traditional feature selection
methods, with the ESA Algorithm excelling in their respective categories. In the second phase, we integrate enhancements into
the traditional SVM model to address uncertainties in medical data, resulting in a more adaptable and accurate classifier. The
enhanced SVM refines both training and testing sets, ensuring that the model is trained on the most relevant and accurate data,
thus improving its performance in uncertain or noisy conditions. Overall, the proposed hybrid framework provides a more robust

and efficient solution for medical data classification, improving both the accuracy and adaptability of SVM classifiers in real-
world applications.