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Improving Machine Learning Classification Predictions through SHAP and Features Analysis Interpretation

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Integrating SHAP values with feature analysis significantly enhances classifier performance by reducing misclassification rates.
Classifiers such as Random Forest, Gradient Boosting Machine, and XGBoost achieved impressive metrics, including MCC values above 0.58.
A misclassification-detection framework based on filtering rules identified misclassified predictions in prostate cancer datasets effectively.
The SHAP approach highlighted important features associated with misclassifications, leading to improved interpretability and performance.

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Integration of SHAP values and feature analysis improves prediction performance in machine learning classifiers for prostate cancer detection.

Bernal et al. (2025) studied this question.

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Integration of SHAP values and feature analysis improves prediction performance in machine learning classifiers for prostate cancer detection.