Ensemble Technique to Predict Breast Cancer on Multiple Datasets.

Background Breast cancer is the most common disease in women, and it is very difficult to detect breast cancer early, so there are fewer reasons to measure women's survival. The purpose of this paper to apply various basic machine learning algorithms on multiple breast cancer datasets and the obtained results combined with an ensemble technique to find whether the datasets getting high accuracy. Methods Five machine learning algorithms such as decision tree, random forest, k-nearest neighbor, support vector clustering (SVC) and logistic regression (LR) are used to achieve high accuracy. In this study, along with five classifiers and voting ensemble methods on three different datasets (Mammographic mass data, Original Wisconsin breast cancer data and Diagnostic Wisconsin breast cancer data) to obtain the predicted accuracy recall, precision and receiver operating characteristic (ROC) curves. The results obtained by three different datasets were compared. Results By applying five classifiers and voting ensemble, the comparison results show that logistic regression, SVC and voting ensemble methods are potentially effective for predicting classification accuracy. The highest value is (Accuracy: 97.13% with ensemble by D2, Precision: 96.90% with ensemble and LR method by D3, Recall: 97.78% with SVC by D3, ROC: 99.93% with LR by D2). Conclusions Machine learning classifiers and ensemble technologies have the potential to identify diseases. The applied method highlights the importance of classifiers and ensemble methods. [ABSTRACT FROM AUTHOR]