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. Our proposed method can be easily implemented in real life and is suitable for easy breast cancer predictions. The dataset was collected from Kaggle. In our model, we have implemented some different classifiers named Random Forest (RF), Logistic Regression (LR), Gradient Boosting (GB), 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 LRB 94.736%, RFB 94.736%, GBB 95.614%, and KNB 92.105% accuracy, Boosting LRBO 96.491%, RFBO 99.122%, and GBBO 98.218% accuracy, and Voting algorithm LRGK with 95.614% accuracy. We have used hyper-parameter tuning in each classifier to assign the best parameters. The experimental study indicates breast cancer predictions with a higher degree of accuracy and evaluated the findings of other current studies, RFBO with 99.122% accuracy being the best performance.