A Comparative Analysis of Machine Learning techniques on Breast Cancer diagnosis using WEKA

Breast cancer is one of the most common malignancies affecting women worldwide, with many fatalities yearly. The risk of death suffered by breast cancer is increasing exponentially. Due to a surge of development of research in the medical field, providing more timely and possible early detection of disease has become a time-demanding option. By far, radiologists have manually checked cancer images and diagnosed them. Research has shown that a considerable number of ultrasound images are created every individual day. However, the number of radiologists is limited, so they cannot provide service on time. However, they often misclassify breast lesions, resulting in a high false-positive rate. An automatic system for detecting disease assists radiologists in disease diagnosis and provides reliable, productive, and reduces the risk of death. In this paper, we compare six machine learning models, namely (i) Support Vector Machine (SVM), (ii) Naive Bayes (NB), (iii) Logistic Regression (LR), (iv) Decision Tree (DT), (v) Random Forest (RF), and (vi) k-Nearest Neighbors (k-NN) on two different datasets (i) the Wisconsin Breast Cancer Dataset (WBCD) and (ii) the Breast Cancer Coimbra Dataset (BCCD). This study aims to create different classification models to analyze the obtained results and compare them to predict breast cancer. We use several performance metrics to select the best classification model among them. Our comparative analysis shows that SVM models can achieve better performance metrics, and thus the model of this research possesses relevant to use in clinical applications.