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In this slide, the workflow of our research study is outlined:
ALN Dataset:
Commencing with the Axillary Lymph Node (ALN) dataset as our foundation.
Feature Extraction:
Conducting feature extraction from the dataset to identify key variables.
Training and Testing Split:
Dividing the dataset into training (80%) and testing (20%) sets for model development and evaluation.
Machine Learning Models:
Applying advanced machine learning models such as Gradient Boosting Machine (GBM), k-Nearest Neighbors (KNN), and Decision Trees (DT).
Performance Measures:
Evaluating model performance through various metrics:
ROC Curve: Assessing the trade-off between sensitivity and specificity.
Accuracy: Measuring overall correctness.
Precision: Calculating the accuracy of positive predictions.
Recall: Identifying the proportion of true positives.
F1 Score: Balancing precision and recall.
Final Classification:
Concluding with the final classification of metastasis and non-metastasis based on the model's predictions.
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IEEE/ICACT20240384 Slide.06
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