Survival Risk Prediction of Esophageal Cancer Based on the Kohonen Network Clustering Algorithm and Kernel Extreme Learning Machine

• Published in: Mathematics (Basel) Vol. 10; no. 9; p. 1367
• Main Authors: Wang, Yanfeng, Wang, Haohao, Li, Sanyi, Wang, Lidong
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.05.2022
• Subjects: Accuracy; Algorithms; AMSSA; Artificial intelligence; Artificial neural networks; Cancer; Cancer therapies; Clustering; Datasets; DNA methylation; Endoscopy; Esophageal cancer; Feature selection; Gene expression; KELM; Kernel functions; Kohonen network clustering; Machine learning; Mathematics; Medical prognosis; Mutation; Optimization; Particle swarm optimization; Patients; Pneumonia; Prediction models; Radiation; Regression analysis; Risk levels; Search algorithms; Statistical analysis; Support vector machines; Survival; survival risk prediction; Swarm intelligence; Variables; China
• ISSN: 2227-7390; 2227-7390
• DOI: 10.3390/math10091367

Accurate prediction of the survival risk level of patients with esophageal cancer is significant for the selection of appropriate treatment methods. It contributes to improving the living quality and survival chance of patients. However, considering that the characteristics of blood index vary with individuals on the basis of their ages, personal habits and living environment etc., a unified artificial intelligence prediction model is not precisely adequate. In order to enhance the precision of the model on the prediction of esophageal cancer survival risk, this study proposes a different model based on the Kohonen network clustering algorithm and the kernel extreme learning machine (KELM), aiming to classifying the tested population into five catergories and provide better efficiency with the use of machine learning. Firstly, the Kohonen network clustering method was used to cluster the patient samples and five types of samples were obtained. Secondly, patients were divided into two risk levels based on 5-year net survival. Then, the Taylor formula was used to expand the theory to analyze the influence of different activation functions on the KELM modeling effect, and conduct experimental verification. RBF was selected as the activation function of the KELM. Finally, the adaptive mutation sparrow search algorithm (AMSSA) was used to optimize the model parameters. The experimental results were compared with the methods of the artificial bee colony optimized support vector machine (ABC-SVM), the three layers of random forest (TLRF), the gray relational analysis–particle swarm optimization support vector machine (GP-SVM) and the mixed-effects Cox model (Cox-LMM). The results showed that the prediction model proposed in this study had certain advantages in terms of prediction accuracy and running time, and could provide support for medical personnel to choose the treatment mode of esophageal cancer patients.