Deep Learning-Based Multi-Modal Ensemble Classification Approach for Human Breast Cancer Prognosis

Abstract

Ensemble models based on deep learning have made significant contributions to the medical field, particularly in the area of disease prediction. Breast cancer is a highly aggressive disease with a high mortality rate. Timely and effective prediction of breast cancer can reduce the risk of it progressing to later stages and the need for unnecessary medications. While previous studies have focused on predicting breast cancer using single-modal datasets, multi-modal datasets that include gene expression (gene exp), clinical, and copy number variation (CNV) data have become available in recent years for predictive model development. However, despite multiple studies using multi-modal data for disease prediction, models designed for breast cancer are typically homogeneous neural networks. This article proposes a heterogeneous deep learning-based ensemble model for effective breast cancer prediction using multi-modal data. The model consists of three phases: feature extraction, stacked feature set creation, and using extracted features as input for a stacked-based model using a random forest algorithm for effective prediction. For feature extraction, convolutional neural networks (CNNs) are used for clinical and gene expression data, and deep neural networks (DNNs) are used for CNV data. The extracted features from CNNs and DNNs are stacked to create a comprehensive feature set. The simulation results demonstrate the superiority of the proposed framework in terms of accuracy compared to uni-modal and homogeneous model-multi-modal frameworks.

Keywords

• Breast cancer
• deep learning
• feature extraction
• machine learning
• prognosis prediction