High Accuracy Predictive Model on Breast Cancer Using Ensemble Approach of Supervised Machine Learning Algorithms

This research article is based on the ensemble approach of different supervised machine learning algorithms to identify the early stages of breast cancer problems. The World Health Organization (WHO) approved that existence of the breast tumor is high for the women in developing countries and it is one of the significant research issues in current scenario in the real world. In this research article researcher used the 30 features to extract and predict accurate prediction on breast cancer using ensemble approach of supervised machine learning algorithms. It is a great challenge in designing a machine learning model to evaluate the performance of the classification of breast tumor. Implementing an efficient classification methodology will support in resolving the complications in analyzing breast cancer. This proposed model employs four machine learning (ML) algorithms Decision tree classifiers, Random Forest KNN, and support vector machine (SVM) and found support vector machine (SVM) which given the high accuracy of 0.976688 among them for the categorization of breast tumor in women. This classification includes the two levels of disease as benign or malignant. The researcher also used the other parameters and evaluated this predictive model using Precision, Recall and F1-Score. The data analysis report is proved that this predictive model is having 98% accuracy level to predict the cancer at early stages in women.