Explainable machine learning models for early gastric cancer diagnosis.
Sci Rep
; 14(1): 17457, 2024 07 29.
Article
em En
| MEDLINE
| ID: mdl-39075116
ABSTRACT
Gastric cancer remains a significant global health concern, with a notably high incidence in East Asia. This paper explores the potential of explainable machine learning models in enhancing the early diagnosis of gastric cancer. Through comprehensive evaluations, various machine learning models, including WeightedEnsemble, CatBoost, and RandomForest, demonstrated high potential in accurately diagnosing early gastric cancer. The study emphasizes the importance of model explainability in medical diagnostics, showing how transparent, explainable models can increase trust and clinical acceptance, thereby improving diagnostic accuracy and patient outcomes. This research not only highlights key biomarkers and clinical features critical for early detection but also presents a versatile approach that could be applied to other medical diagnostics, promoting broader adoption of machine learning in clinical settings.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Neoplasias Gástricas
/
Detecção Precoce de Câncer
/
Aprendizado de Máquina
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article