Endoscopy report mining for intelligent gastric cancer screening. Issue 3 (10th December 2019)

• Record Type: Journal Article
• Authors: Pan, Jinxin; Ding, Shuai; Yang, Shanlin; Li, Gang; Liu, Xiao
• Other Names: Saen Reza Farzipoor guestEditor.; Song Malin guestEditor.; Fisher Ron guestEditor.
• Is Part Of: Expert systems. Volume 37:Issue 3(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-12-10
• Subjects: gastric cancer screening -- hyperparameters -- multiobjective optimization -- neural networks -- NSGA‐II; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12504
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: Endoscopy is an important tool for gastric cancer screening. Due to the lack of effective decision support system for endoscopy, the detection of gastric cancer in the clinic is usually with low sensitivity. In this paper, we propose a Genetic Algorithm optimized Neural Network (GAoNN) approach for gastric cancer detection based on endoscopy reports mining. Considering the fact that gastric cancer sensitivity can significantly improve the 5‐year survival rate of patients, both the prediction accuracy and the sensitivity are employed to construct a multiobjective optimization model for enhancing the classification performance of GAoNN. In particular, we extended an effective genetic algorithm Nondominated Sorting Genetic Algorithm II (NSGA‐II) to train a neural network and reduced the complexity in training hyperparameters and improved the efficiency by substituting the computationally intensive stochastic gradient descent (SGD) algorithm in a neural network. Specifically, we designed the novel crossover and mutation operators and modified the nondominated ranking and crowding distance sorting procedures in NSGA‐II for GAoNN. Through testing on 8, 546 real‐world endoscopy reports, we show that GAoNN achieves a prediction accuracy up to 83.74%, which is better than several competitors by significantly increasing sensitivity to 83.14%. GAoNN also reduces the training time by 30.94% when compared with conventional SGD‐based training, which indicates the feasibility of GAoNN in clinical practice.