Machine learning to predict the cancer-specific mortality of patients with primary non-metastatic invasive breast cancer.
Surg Today
; 51(5): 756-763, 2021 May.
Article
en En
| MEDLINE
| ID: mdl-33104877
ABSTRACT
PURPOSE:
We used five machine-learning algorithms to predict cancer-specific mortality after surgical resection of primary non-metastatic invasive breast cancer.METHODS:
This study was a secondary analysis of data for 1661 women with primary non-metastatic invasive breast cancer. The overall patient population was divided into a training group and a test group at a ratio of 82 and python was used for machine learning to establish the prognosis model.RESULTS:
The machine-learning Gbdt algorithm for cancer-specific death caused by various factors showed the five most important factors, ranked from high to low as follows the number of regional lymph node metastases, LDH, triglyceride, plasma fibrinogen, and cholesterol. Among the five algorithm models in the test group, the highest accuracy rate was by DecisionTree (0.841), followed by the gbm algorithm (0.838). Among the five algorithms, the AUC values from high to low were GradientBoosting (0.755), gbm (0.755), Logistic (0.733), Forest (0.715), and DecisionTree (0.677).CONCLUSION:
Machine learning can predict cancer-specific mortality after surgery for patients with primary non-metastatic invasive breast.Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Neoplasias de la Mama
/
Aprendizaje Automático
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Límite:
Female
/
Humans
Idioma:
En
Revista:
Surg Today
Año:
2021
Tipo del documento:
Article
País de afiliación:
China