Asan M. E., Kubat C.
IMSS25 International Symposium, Düzce, Türkiye, 25 - 27 Eylül 2025, ss.21-35, (Tam Metin Bildiri)
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Yayın Türü:
Bildiri / Tam Metin Bildiri
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Doi Numarası:
10.5281/zenodo.17530689
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Basıldığı Şehir:
Düzce
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Basıldığı Ülke:
Türkiye
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Sayfa Sayıları:
ss.21-35
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İstanbul Gelişim Üniversitesi Adresli:
Evet
Özet
ABSTRACT
The early and accurate detection of thyroid cancer is crucial for informing clinical decisions
and improving patient outcomes. Fine-tuning binary classification algorithms through
hyperparameter adjustments is essential for improving the performance and predictive accuracy of
machine learning models in diagnostic processes. This study examines the effect of hyperparameter
tuning on the performance of binary classification algorithms using a real-world dataset of thyroid
cancer, comprising ultrasound, cytology, and blood test data. This investigation is a comprehensive
case study on enhancing various binary classification algorithms in predicting thyroid cancer, a vital
component of medical diagnostics. We apply three tuning methods — grid search and random search
— in conjunction with three commonly utilized classification algorithms: Random Forest, Support
Vector Machines (SVM), and Logistic Regression. Model performance is assessed using Accuracy,
ROC-AUC Score, Confusion Matrix, and ROC Curves before and following tuning. The findings
demonstrate that the optimized models significantly outperform their default counterparts, with
hyperparameter tuning influencing the rankings of model selection. These results underscore the need
to integrate hyperparameter optimization into the machine learning workflow, particularly in critical
domains such as medical diagnostics. Moreover, we investigate the impact of data preprocessing,
feature selection, and cross-validation techniques on the model outcomes.