A Random Forest Model for Breast Cancer Classification: BUILDING AND OPTIMIZATION OF RANDOM FOREST MODEL FOR CLASSIFICATION OF BREAST CANCER INTO BENIGN AND MALIGNANT - Tapa blanda

Gumel, A M; Adam, A N; Waziri, I Z

 
9786208449407: A Random Forest Model for Breast Cancer Classification: BUILDING AND OPTIMIZATION OF RANDOM FOREST MODEL FOR CLASSIFICATION OF BREAST CANCER INTO BENIGN AND MALIGNANT
Tapa blanda
ISBN 10: 6208449405 ISBN 13: 9786208449407
Editorial: LAP Lambert Academic Publishing, 2025

Sinopsis

This book highlights on development and optimization of a Random Forest (RF) model to classify breast cancer as benign or malignant using the Wisconsin Breast Cancer Dataset. After preprocessing 569 samples (357 benign, 212 malignant), a default RF model achieved 95.61% accuracy. To improve results, hyperparameter tuning via Grid Search was applied, adjusting parameters such as number of trees (150), max depth (None), min samples split (2), min samples leaf (1), and random seed (123). The optimized RF model achieved 99.12% accuracy, precision, recall, and F1-score, outperforming other methods like SVM, XGBoost, and prior RF implementations. Results show reduced false negatives and no false positives, indicating high sensitivity and specificity. The work underscores the value of meticulous hyperparameter tuning in medical AI applications and suggests future integration with neural networks and hybrid models for enhanced performance in clinical breast cancer diagnosis.

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Editorial
LAP Lambert Academic Publishing
Año de publicación
2025
Idioma
Inglés
ISBN 10 
6208449405
ISBN 13 
9786208449407
Encuadernación
Tapa blanda
Número de páginas
52
Contacto del fabricante
no disponible
Persona responsable
no disponible