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This slide details the implementation of three machine learning models:
Decision Tree Classifier:
Crucial for predicting lymph node metastasis based on input features.
Develops rules using factors like tumor size, location, and age.
Classifies patients into groups with or without lymph node metastases.
K-Nearest Neighbors (KNN):
Predicts metastasis by identifying 'k' nearest patients with similar variables.
Non-parametric and logic-based, relying on the dominant class among neighbors.
Critical choice of "k" value and distance metric for reliable predictions.
Gradient Boosting Machine (GBM):
Iteratively trains decision trees, compensating for prediction errors.
Fits trees to residuals in repeated cycles, capturing complex patterns.
Enhances the model through an iterative process for a reliable assessment of lymph node metastasis likelihood.
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IEEE/ICACT20240384 Slide.10
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