Enhanced PSO feature selection with Runge-Kutta and Gaussian sampling for precise gastric cancer recurrence prediction.
Comput Biol Med
; 175: 108437, 2024 Jun.
Article
em En
| MEDLINE
| ID: mdl-38669732
ABSTRACT
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.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Temas:
Geral
/
Tipos_de_cancer
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Estomago
Base de dados:
MEDLINE
Assunto principal:
Neoplasias Gástricas
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Recidiva Local de Neoplasia
Limite:
Humans
Idioma:
En
Revista:
Comput Biol Med
Ano de publicação:
2024
Tipo de documento:
Article