ECMHA-PP: A Breast Cancer Prognosis Prediction Model Based on Energy-Constrained Multi-Head Self-Attention.

Abstract

Purpose: Breast cancer is a significant threat to women's health. Precise prognosis prediction for breast cancer can help doctors implement more rational treatment strategies. Artificial intelligence can assist doctors in decision-making and enhance prediction accuracy.

Experimental design: In this paper, a deep learning model ECMHA-PP (Energy Constrained Multi-Head Self-Attention based Prognosis Prediction) is proposed to predict the prognosis of breast cancer. ECMHA-PP utilizes patients' clinical data and extracts features through a cross-position mix and a channel mix multi-layer perceptron. Then, it incorporates an energy-constrained multi-head self-attention layer to improve feature extraction capability. The source code of ECMHA-PP has been hosted on GitHub and is available at https://github.com/xiaoliu166370/ECMHA-PP.

Results: To evaluate our proposed method, prognostic prediction experiments were performed on the METABRIC dataset, yielding outstanding results with an average accuracy of 93.0% and an average area under the curve of 0.974. To further validate the model's performance, we conducted tests on another independent dataset, BRCA, achieving an accuracy of 87.6%.

Conclusions and clinical relevance: In comparison with other widely used advanced methods, ECMHA-PP demonstrated higher comprehensive performance, making it a reliable prognostic prediction model for breast cancer. Given its robust feature extraction and prediction capabilities.