Comparison of machine learning models on breast cancer risk prediction challenge

Abstract

Breast cancer is a malignant tumor that poses a serious risk to women's life and wellbeing. To make matters worse, this cancer is less symptomatic in its early stages. It is not easily diagnosed through traditional means. The topic of this essay is to investigate machine learning for the determination of breast cancer.. The methods based on machine learning are as followed: automated nuclear section segmentation model, BCRecommender System, DNNs, and computer-aided diagnosis model (CADM). The methods studied are all based on the BreCaHad dataset and use a comparative metric.To measure the performance of each model, the accuracy, F1-score, specificity, and precision are used. The result shows that the approaches based on machine learning work well in diagnosing breast carcinoma, with high accuracy. Most of them have a percentage over 90% in accuracy and some of them are even higher than 95%. However, some of the models work poorly, such as layer 1 of BCRecommender with 61.06% accuracy and EfficientNetB0 with 72.96%. With every aspect taken into consideration, computer-aided diagnosis (CADM: Using combined features of HOG, WPD, ResNet as well as PCA + SVM) has the greatest advantage in diagnosing BC.