A Machine Learning-Based Traditional and Ensemble Technique for Predicting Breast Cancer

Abstract

Breast cancer is a physical disease and increasing in recent years. The topic is known widely in the recent world. Most women are suffering from problem of breast cancer. The disease is measured by the differences between normal and affected area ratio and the rate of uncontrolled increase of the tissue. Many studies have been conducted in the past to predict and recognize breast cancer. We have found some good opportunities to improve the technique. We propose predicting the risks and making early awareness using effective algorithm models. The dataset was collected from Kaggle [9]. We have used Random Forest, Logistic Regression, Gradient Boosting, and K-Nearest Classifier Algorithms. Logistic Regression and Random Forest Classifier were performed well with 98.245% testing accuracy. Other algorithms like Gradient Boosting 91.228%, and K-Nearest 92.105% testing accuracy. We also used some different ensemble models to justify the performances. We have used Bagging, Boosting, and Voting algorithms. To assign the optimal parameters to each classifier, we employed hyper-parameter tweaking. The experimental investigation demonstrated more precise breast cancer forecasts and assessed the outcomes of previous recent studies, with the greatest performance being 99.122% accuracy.