BMC medical informatics and decision making · 2025
Optimizing unsupervised feature engineering and classification pipelines for differentiated thyroid cancer recurrence prediction.
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Onah Emmanuel, Eze Uche Jude, Abdulraheem Abdullahi Salahudeen, Ezigbo Ugochukwu Gabriel, Amorha Kosisochi Chinwendu, Ntie-Kang Fidele
Аннотация
BACKGROUND: Differentiated thyroid cancer (DTC) is a common endocrine malignancy with rising incidence and frequent recurrence, despite a generally favorable prognosis. Accurate recurrence prediction is critical for guiding post-treatment strategies. This study aimed to enhance predictive performance by refining feature engineering and evaluating a diverse ensemble of machine learning models using the UCI DTC dataset.
METHODS: Unsupervised data engineering-specifically dimensionality reduction and clustering-was used to improve feature quality. Principal Component Analysis (PCA) and Truncated Singular Value Decomposition (t-SVD) were selected based on superior clustering metrics: adjusted Rand Index (ARI > 0.55) and V-measure (> 0.45). These were integrated into classification pipelines using Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), Feedforward Neural Network (FNN), and Gradient Boosting (GB). Model performance was evaluated through bootstrapping on an independent test set, stratified 10-fold cross-validation (CV), and subgroup analyses. Metrics included balanced accuracy, F1 score, AUC, sensitivity, specificity, and precision, each reported with 95% confidence intervals (CIs). SHAP analysis supported model interpretability.
RESULTS: The PCA-based LR pipeline achieved the best test set performance: balanced accuracy of 0.95 (95% CI: 0.90-0.99), AUC of 0.99 (95% CI: 0.97-1.00), and sensitivity of 0.94 (95% CI: 0.84-1.00). In stratified CV, it maintained strong results (balanced accuracy: 0.86; AUC: 0.97; sensitivity: 0.80), with consistent performance across clinically relevant subgroups. The t-SVD-based LR pipeline showed comparable performance on both test and CV sets. SVM and FNN pipelines also performed robustly (test AUCs > 0.99; CV AUCs > 0.96). RF and KNN had high specificity but slightly lower sensitivity (test: ~0.87; CV: 0.77-0.80). GB pipelines showed the lowest overall performance (test balanced accuracy: 0.86-0.88; CV: 0.85-0.88).
CONCLUSIONS: Dimensionality reduction via PCA and t-SVD significantly improved model performance, particularly for LR, SVM, FNN, RF and KNN classifiers. The PCA-based LR pipeline showed the best generalizability, supporting its potential integration into clinical decision-support tools for personalized DTC management.
CLINICAL TRIAL NUMBER: Not applicable.
METHODS: Unsupervised data engineering-specifically dimensionality reduction and clustering-was used to improve feature quality. Principal Component Analysis (PCA) and Truncated Singular Value Decomposition (t-SVD) were selected based on superior clustering metrics: adjusted Rand Index (ARI > 0.55) and V-measure (> 0.45). These were integrated into classification pipelines using Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), Feedforward Neural Network (FNN), and Gradient Boosting (GB). Model performance was evaluated through bootstrapping on an independent test set, stratified 10-fold cross-validation (CV), and subgroup analyses. Metrics included balanced accuracy, F1 score, AUC, sensitivity, specificity, and precision, each reported with 95% confidence intervals (CIs). SHAP analysis supported model interpretability.
RESULTS: The PCA-based LR pipeline achieved the best test set performance: balanced accuracy of 0.95 (95% CI: 0.90-0.99), AUC of 0.99 (95% CI: 0.97-1.00), and sensitivity of 0.94 (95% CI: 0.84-1.00). In stratified CV, it maintained strong results (balanced accuracy: 0.86; AUC: 0.97; sensitivity: 0.80), with consistent performance across clinically relevant subgroups. The t-SVD-based LR pipeline showed comparable performance on both test and CV sets. SVM and FNN pipelines also performed robustly (test AUCs > 0.99; CV AUCs > 0.96). RF and KNN had high specificity but slightly lower sensitivity (test: ~0.87; CV: 0.77-0.80). GB pipelines showed the lowest overall performance (test balanced accuracy: 0.86-0.88; CV: 0.85-0.88).
CONCLUSIONS: Dimensionality reduction via PCA and t-SVD significantly improved model performance, particularly for LR, SVM, FNN, RF and KNN classifiers. The PCA-based LR pipeline showed the best generalizability, supporting its potential integration into clinical decision-support tools for personalized DTC management.
CLINICAL TRIAL NUMBER: Not applicable.