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A RANDOM FOREST MODEL FOR BREAST CANCER CLASSIFICATION

By Gumel, A M

A RANDOM FOREST MODEL FOR BREAST CANCER CLASSIFICATION

By Gumel, A M

0 - Default Title
Description
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.
Product details
Binding:
Paperback
Number of Pages:
52
Release Date:
2025-08-19
Publication Date:
2025-08-19
Publisher:
LAP LAMBERT Academic Publishing
Languages:
Original: English
ISBN10:
6208449405
ISBN13:
9786208449407
GPSR Manufacturer Reference:
[email protected]
Weight:
96 g
Height:
150 cm
Width:
220 cm
Thickness:
4 cm
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