Enhancing the Prediction of Gliomas Disease with an Optimistic Ensemble Approach

This study employs an ensemble technique in artificial intelligence models to increase the forecast accuracy of gliomas, a frequent and aggressive kind of brain tumor. Because of their varied character, gliomas provide major diagnostic hurdles. Therefore, early and precise diagnosis is essential for efficient treatment planning and better patient outcomes. While classical machine learning methods have proven useful in glioma prediction, there is still much room for improvement. In this research, we present an advanced ensemble method that combines many machine learning methods to develop a prediction model that is more reliable and accurate. An efficient machine learning model is proposed using the optimistic ensemble approach which proposes an accuracy of 88%. We achieve this by combining different base classifiers and making use of their particular advantages and weaknesses to create a collective wisdom that improves predicted performance. Our ensemble approach outperformed traditional standalone models in terms of predicted accuracy, sensitivity, and specificity after rigorous evaluation and verification using real-world clinical datasets. The results of this study highlight the possibility of ensemble learning to improve glioma predictive models and enable prompt and accurate diagnostic making choices, which is critical for maximizing treatment approaches and enhancing patient outcomes.