Breast Cancer Risk Prediction and IDC Tumor Classification in Different Stages Based on Machine Learning Models

Abstract

Based on the medical statistical analyses of relevant information, in recent years, breast cancer is one of the leading causes of cancer-related deaths in women worldwide. Hence, the early detection and accurate diagnosis of breast cancer patients is significant to improve their prognosis. In this study, we propose a multi-modal, data-driven intelligent framework especially designed to breast cancer analysis the Multi-Dimensional Feature Refinement Cancer Network (MDFR-Cancer-Net) that can be used to analyse breast cancer at various clinical stages based on multi-modal data.The framework of this study will be to combine tabular clinical data for risk stratification, pathological imaging data of tumors and mi-RNA molecular data for biomarker-assisted analysis at different times in the study. MDFR-Cancer-Net will first be used to reduce the number of features and improve the representation ability of features. A few are Naive Bayes and Deep U-Net; the rest are other types of models for classification and segmentation. Five-fold cross-validation and ablation tests were carried out to obtain the above results. Based on the above experimental results, the accuracy of the Naive Bayes model on the Wisconsin Breast Cancer dataset was 99%, and that of the baseline SVM was 92%. Deep U-Net reached an accuracy of 96% for the 277,524 IDC pathological image patches and exceeded the baseline CNN's accuracy of 87%. Naive Bayes had 100% accuracy in the mi-RNA molecular analysis and outperformed the baseline Random Forest (95%). In short, the framework presented here shows that all kinds of data can be used together to help diagnose breast cancer.