Endoscopy report mining for intelligent gastric cancer screening

• Published in: Expert systems Vol. 37; no. 3
• Main Authors: Pan, Jinxin, Ding, Shuai, Yang, Shanlin, Li, Gang, Liu, Xiao
• Format: Journal Article
• Language: English
• Published: Oxford Blackwell Publishing Ltd 01.06.2020
• Subjects: Accuracy; Cancer; Crossovers; Decision support systems; Endoscopy; Gastric cancer; gastric cancer screening; Genetic algorithms; hyperparameters; Medical screening; multiobjective optimization; Multiple objective analysis; Neural networks; NSGA‐II; Optimization; Sensitivity; Sorting algorithms; Training
• ISSN: 0266-4720; 1468-0394
• DOI: 10.1111/exsy.12504

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.