Survival risk prediction of gastric cardia cancer-based on a dynamic modular neural network

• Published in: Systems science & control engineering Vol. 12; no. 1
• Main Authors: Lu, Chao, Li, Yang, Wei, Xing
• Format: Journal Article
• Language: English
• Published: Macclesfield Taylor & Francis 31.12.2024; Taylor & Francis Ltd; Taylor & Francis Group
• Subjects: Adaptive algorithms; Algorithms; Cancer; Clustering; data-driven; gastric cardia; Medical prognosis; modular neural network; Neural networks; Particle swarm optimization; Prognosis; prognostic prediction; Survival; Survival risk
• ISSN: 2164-2583; 2164-2583
• DOI: 10.1080/21642583.2024.2328542

Gastric cardia cancer is a high-incidence malignant tumour, which seriously endangers human health and life safety. The patient prognosis of gastric cardia cancer is affected by diet, physical condition, regional environment, medical history and other factors. Traditional prediction methods cannot fully reflect the prognosis characteristics and survival risks of all patients. Therefore, this paper proposes a data-driven method for the survival risk of cardiac cancer based on an adaptive particle swarm optimization algorithm (APSO) and a dynamic modular neural network (DMNN). First, the article uses density clustering to cluster 293 patients' blood characteristics and generate different sub-networks. Second, the weight is calculated through the APSO algorithm and the sub-network output is obtained by the integration algorithm. At last, the effectiveness of this network is verified through a 50% cross-validation of training sets and test sets. The results show that the survival prediction based on the APSO-DMNN data-driven method shows good classification performance and accuracy.