Enhanced PSO feature selection with Runge-Kutta and Gaussian sampling for precise gastric cancer recurrence prediction

• Published in: Computers in biology and medicine Vol. 175; p. 108437
• Main Authors: Zhao, Jungang, Li, JiaCheng, Yao, Jiangqiao, Lin, Ganglian, Chen, Chao, Ye, Huajun, He, Xixi, Qu, Shanghu, Chen, Yuxin, Wang, Danhong, Liang, Yingqi, Gao, Zhihong, Wu, Fang
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01-06-2024; Elsevier Limited
• Subjects: Algorithms; Artificial intelligence; Biomarkers; Cancer; Comparative analysis; Datasets; Early warning systems; Experiments; Feature selection; Fuzzy k-nearest neighbor; Gastric cancer; Gaussian sampling; Global optimization; Humans; Infections; Invasiveness; Machine learning; Metastasis; Mortality; Neoplasm Recurrence, Local; Normal Distribution; Optimization; Particle swarm optimization; Risk factors; Runge-kutta; Runge-Kutta method; Sampling; Stomach Neoplasms - pathology; Global optimization; Gaussian sampling; Fuzzy k-nearest neighbor; Gastric cancer; Particle swarm optimization; Runge-kutta
• ISSN: 0010-4825; 1879-0534
• DOI: 10.1016/j.compbiomed.2024.108437

Gastric cancer (GC), characterized by its inconspicuous initial symptoms and rapid invasiveness, presents a formidable challenge. Overlooking postoperative intervention opportunities may result in the dissemination of tumors to adjacent areas and distant organs, thereby substantially diminishing prospects for patient survival. Consequently, the prompt recognition and management of GC postoperative recurrence emerge as a matter of paramount urgency to mitigate the deleterious implications of the ailment. This study proposes an enhanced feature selection model, bRSPSO-FKNN, integrating boosted particle swarm optimization (RSPSO) with fuzzy k-nearest neighbor (FKNN), for predicting GC. It incorporates the Runge-Kutta search, for improved model accuracy, and Gaussian sampling, enhancing the search performance and helping to avoid locally optimal solutions. It outperforms the sophisticated variants of particle swarm optimization when evaluated in the CEC 2014 test suite. Furthermore, the bRSPSO-FKNN feature selection model was introduced for GC recurrence prediction analysis, achieving up to 82.082 % and 86.185 % accuracy and specificity, respectively. In summation, this model attains a notable level of precision, poised to ameliorate the early warning system for GC recurrence and, in turn, advance therapeutic options for afflicted patients.
•Improves model accuracy through Runge-Kutta search and Gaussian sampling.•Outperforms sophisticated PSO variants on the CEC 2014 test suite, indicating better optimization capabilities.•Presents bRSPSO-FKNN as a feature selection model with high accuracy in gastric cancer prediction.•Offers a highly precise model to improve early warning systems for gastric cancer and advance treatment options.