Enhanced Glioma Disease Prediction Through Ensemble-Optimized Machine Learning

In this work, we utilize an ensemble-optimized machine learning architecture to present a novel method for glioma illness prediction. Glioma is a kind of tumor that affects the brain and spinal cord. Because of its modest variations in symptoms and complexity, diagnosing this tumor type early on can be extremely difficult. Taking note of these difficulties, we have concentrated our efforts on integrating several machine learning models to improve the glioma diagnosis' prediction accuracy and dependability. Using a combination of optimization methods and feature selection approaches, we carefully hone our model to manage the complexities of glioma data. Several predictive algorithms, including decision trees, support vector machines, and neural networks, are incorporated into our ensemble model; each approach brings a distinct strength to the total prediction ability. The proposed model reaches a remarkable 90% accuracy through thorough training and validation on a large dataset of radiological and clinical variables. This important development highlights how ensemble-optimized machine learning may change the landscape of glioma illness prediction and present physicians with a useful tool for glioma patient early identification and treatment planning.