Performance Analysis of XGBoost Ensemble Methods for Survivability with the Classification of Breast Cancer

Breast cancer (BC) disease is the most common and rapidly spreading disease across the globe. This disease can be prevented if identified early, and this eventually reduces the death rate. Machine learning (ML) is the most frequently utilized technology in research. Cancer patients can benefit from early detection and diagnosis. Using machine learning approaches, this research proposes an improved way of detecting breast cancer. To deal with the problem of imbalanced data in the class and noise, the Synthetic Minority Oversampling Technique (SMOTE) has been used. There are two steps in the suggested task. In the first phase, SMOTE is utilized to decrease the influence of imbalance data issues, and subsequently, in the next phase, data is classified using the Naive Bayes classifier, decision trees classifier, Random Forest, and their ensembles. According to the experimental analysis, the XGBoost-Random Forest ensemble classifier outperforms with 98.20% accuracy in the early detection of breast cancer.

Automated summary

Breast cancer is the most common and rapidly spreading disease globally, and machine learning has been proposed as an effective tool for early detection and diagnosis. The use of Synthetic Minority Oversampling Technique (SMOTE) has been shown to improve classification accuracy, particularly when dealing with imbalanced data issues.