Machine Learning-Based Relative Performance Analysis for Breast Cancer Prediction

The current high population growth in medical research has made early disease identification an urgent issue. The danger of dying from breast cancer is increasing exponentially along with the rapid population expansion. The second most serious cancer among those that have already been identified is breast cancer. Breast cancer poses a major hazard to women, as it is highly morbid and lethal. Doctors find it challenging to develop a treatment strategy that could increase patient survival time due to the lack of reliable prognostic models. As a result, developing a technique that results in the fewest errors while increasing accuracy takes time. A reliable, effective, and prompt response is offered by an automatic illness detection system, which also helps medical workers identify diseases and reduces the likelihood of fatalities. In this work, we investigate eight machine learning techniques, including GaussianNB, Decision Tree, K-Nearest Neighbor, Random Forest, support vector machine (SVM), XGBoost, LightGBM, AdaBoost. The Wisconsin Breast Cancer dataset was discovered in the UCI machine learning database, a well-known machine learning database. The performance of the study is assessed with relation to accuracy, precision, recall, F1 score, and ROC score. Among the eight-machine learning model, Random Forest and AdaBoost perform best which provides 99.20% accuracy and around 99% ROC curve score.