A RANDOM FOREST MODEL FOR BREAST CANCER CLASSIFICATION: BUILDING AND OPTIMIZATION OF RANDOM FOREST MODEL FOR CLASSIFICATION OF BREAST CANCER INTO BENIGN AND MALIGNANT - Softcover

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

Softcover

ISBN 10: 6208449405 ISBN 13: 9786208449407

Verlag: LAP LAMBERT Academic Publishing, 2025

Inhaltsangabe

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.

Über diesen Titel

Über die Autorin bzw. den Autor

Professor Ahmad Mohammed Gumel is a registered biotechnologist specializing in biocatalysis and bioprocess technology. A global research consultant and journal board member, he has 1,260+ citations, h-index 18, and i10-index 23, with memberships in leading biotech federations worldwide.

Details zur Ausgabe

• Verlag: LAP LAMBERT Academic Publishing
• Erscheinungsdatum: 2025
• Sprache: Englisch
• ISBN 10: 6208449405
• ISBN 13: 9786208449407
• Einband: Taschenbuch
• Anzahl der Seiten: 52
• Kontakt zum Hersteller: preigu GmbH & Co. KG; mail@preigu.de; ; Lengericher Landstr. 19; Osnabrück; Niedersachsen; 49078; Deutschland