Enhancing cancer detection and classification with ensemble machine learning approaches
Suman Kumar Swarnkar, Rohit Dixit
Abstract
Innovations in cancer detection and categorization are urgently needed as cancer is still a major killer on a global scale. Using ensemble machine learning (ML) techniques, we investigate how well they can identify and categorize different kinds of cancer. One strong strategy for dealing with the problems caused by the diverse and unpredictable nature of cancer data is to use ensemble techniques. These approaches integrate many ML algorithms to enhance prediction performance. We conduct a comprehensive analysis of various ensemble techniques, including Bagging, Boosting, and Stacking, to determine their effectiveness in cancer detection and classification. Our study leverages diverse datasets, encompassing different cancer types and stages, to ensure the generalizability of our findings. Through rigorous experimentation and cross-validation, we demonstrate that ensemble learning significantly improves the accuracy, sensitivity, and specificity of cancer diagnosis compared to individual ML models. Furthermore, we delve into the interpretability of ensemble models, providing insights into the most influential features for cancer classification. Our results highlight the potential of ensemble learning to support early diagnosis, personalized treatment plans, and improved patient outcomes in oncological healthcare. This research underscores the critical role of advanced ML techniques in the ongoing battle against cancer, paving the way for more accurate and reliable diagnostic tools.