A One-Dimensional Probabilistic Convolutional Neural Network for Prediction of Breast Cancer Survivability. (23rd July 2021)

• Record Type: Journal Article
• Authors: Salehi, Mohsen; Razmara, Jafar; Lotfi, Shahriar; Mahan, Farnaz
• Is Part Of: Computer journal. Volume 65:Number 10(2022)
• Page Start: 2641
• Page End: 2653
• Publication Date: 2021-07-23
• Subjects: machine learning -- prediction -- breast cancer survivability -- deep learning -- convolutional neural networks; Computers -- Periodicals; 005.1
• DOI: 10.1093/comjnl/bxab096
• Languages: English
• ISSNs: 0010-4620

Abstract

Abstract: Today, machine learning plays a major role in different branches of the healthcare industry, from prognosis and diagnosis to drug development providing a significant perspective on the medical landscape for disease prevention or treatment and the improvement of human life. Recently, the use of deep neural networks in different machine learning applications has shown a great contribution to the improvement of the accuracy of predictions. In this paper, a novel application of convolutional neural networks on medical prognosis is presented. The proposed method employs a one-dimensional convolutional neural network (1D-CNN) to predict the survivability of breast cancer patients. After further examining the network architecture, a number of 8, 14 and 24 convolutional filters were considered within three layers, respectively, followed by a max-pooling layer after the second and third layers. In addition, regarding the probabilistic nature of the survivability prediction problem, an extra layer was added to the network in order to calculate the probability of the patient survivability. To train the developed 1D-CNN machine, the SEER database as the most reliable repository of cancer survivability was used to retrieve the required training set. After a pre-processing to remove unusable records, a set of 50 000 breast cancer cases including 35 features was prepared for training the machine. Based on the results obtained in this study, the developed machine could reach an accuracy of 85.84%. This accuracy is the highest level of accuracy compared to the previous prediction methods. Furthermore, the mean squared error of the calculated probability was 0.112, which is an acceptable value of error for a probability calculation machine. The output of the developed machine can be used reliably by physicians to make decision about the most appropriate treatment strategy.