Early prediction of gallstone disease with various feature selection methods from bioimpedance and laboratory data
International Conference on Next-Generation Engineering Technologies (ISNET'2025), Antalya, Türkiye, 30 Ekim - 01 Kasım 2025, ss.60-61, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Basıldığı Şehir: Antalya
- Basıldığı Ülke: Türkiye
- Sayfa Sayıları: ss.60-61
- Kütahya Sağlık Bilimleri Üniversitesi Adresli: Evet
Özet
Machine learning-based approaches can be used for early prediction of gallstone disease. This study investigates the effect of five different feature selection methods to reduce features and classify bioimpedance and laboratory data by comparing lower-dimensional matrices using well-known classifier algorithms. A publicly available dataset, which consist of 319 samples with 38 features, is used in analysis. The reduced feature sets were obtained using Principal Component Analysis (PCA), One-Way Variance Anaylsis (ANOVA), Minimum Redundancy Maximum Relevance (mRMR), Kruskal-Wallis (KW), and Chi-Square test (X2) methods. Nine distinct classifiers are used to classify all feature sets. A maximum accuracy of 90.48% was obtained using X2 method and Efficient Linear Support Vector Machine classifier.