Enhancing Breast Cancer Detection with Ensemble Machine Learning Models

Abstract

Breast cancer remains a significant public health issue worldwide, underlining the need for accurate and efficient diagnostic methods. In this paper, we propose a new technique to enhance breast cancer diagnosis through the integration of multiple machine-learning models. Our strategy employs a combination of the Naive Bayes classifier, Stochastic Gradient Descent (SGD), Bagging, and the ZeroR classifier, alongside Bayes Network learning. The cornerstone of our approach is Bayes Network learning, a probabilistic graphical model designed to map out the intricate interconnections among various diagnostic factors. This is significant in the way that it can help to uncover complex relationships in the data for the sake of leading to more accurate predictions. Added to the above, we use the Naïve Bayes classifier, a classifier showing good validity in classification tasks and based on probabilistic reasoning, for the screening of breast cancer. Further, a refined model's parameter is included using the SGD and leads to enhancement of the generalization and overall performance of the model. In addition, as part of controlling overfitting, one can also use Bagging. It is an ensemble method in the sense that it considers several models. ZeroR classifier is a very basic classifier and is just used to compare its performance with our composite approach. We are comparing complex ensemble results to its simplicity. We will validate the ability of our proposed methodology to compare the performance of our integrated models against ZeroR.