Enhancing Mesothelioma Cancer Diagnosis through Ensemble Learning Techniques

In the relentless battle against mesothelioma, a rare and aggressive form of cancer with a dire prognosis, this research explores the transformative potential of ensemble learning techniques, namely Bagging Tree, Random Forest, and Ensemble Extra Tree, in revolutionizing the diagnosis of mesothelioma. Traditional diagnostic methods have struggled with late-stage detection and limited treatment options, but this study presents a paradigm shift, leveraging advanced ML algorithms to process treasure of persistent information, comprising clinical records, imaging scans, and biomarker profiles. The Bagging Tree algorithm, Random Forest, and Ensemble Extra Tree are harnessed to create a diverse ensemble, extract informative features, and enhance the predictive power of the diagnostic model. Through meticulous experimentation, this research demonstrates the unprecedented accuracy of the ensemble approach in distinguishing mesothelioma from other thoracic diseases, offering hope for early intervention and personalized treatment strategies. Additionally, the model interpretability provides valuable insights for clinicians, bridging the gap between artificial intelligence and human expertise, and fostering trust in the integration of these cutting-edge tools into clinical practice.