Ilim ha’m ja’miyet 1-1-сан (2026) · 24-26-беттер
HIGH-PRECISION AI PIPELINE EXPLAINED IN PROSTATA CANCER
Madolimov, F., Мадолимов, Ф., Madolimov, F.
Аннотация
This study presents a comprehensive feature engineering process for the early detection of prostate cancer using machine learning methodology. The dataset consisted of key clinical indicators — PSA level, patient age, prostate volume, Gleason score, and clinical stage — which were processed using ANOVA, Chi-square, PCA, RFE, LASSO, and SHAP techniques. The primary objective was to identify the most influential diagnostic features that improve model performance and ensure interpretability.
prostata saratonimashinaviy o‘rganishfeature engineeringPCASHAPdiagnostikaрак предстательной железымашинное обучениеинженерия признаковPCA
Метадайындар булагы: журналдын OAI-PMH архиви · Sindex толук текстти сактабайт, булакка шилтеме берет.