Breast Cancer Detection: Comparative Analysis of Machine Learning Classification Techniques

In the contemporary world, the early detection of any disease has become imperative. With an accelerating rate of population, the chance of fatality by breast cancer is growing exponentially. A reliable and effective detection system helps the medical personnel in fast detection of cancer. In the course of the present study, we have presented a comparative analysis of recent state-of the-art machine learning techniques that are being extensively used in cancer detection especially Breast Cancer by using the breast cancer dataset named Wisconsin dataset. We have statistically and comparatively scrutinized and compared the machine learning techniques that are used in classification like Naïve Bayes (NB), K-Nearest Neighbor (KNN), Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), XGboost (XG) and Decision Tree (DT) for computing the accuracy in the light of performance metrics like recall, precision F1 score and accuracy percentage. Moreover, these classification techniques were also projected on ROC Curve. As a result, this research paper evaluates that the accuracy obtained by XGboost is 98.24% whereas in SVM the accuracy is 96.49%.