Ilim ha’m ja’miyet 1-1-нөмір (2026) · 24-26-беттер
HIGH-PRECISION AI PIPELINE EXPLAINED IN PROSTATA CANCER
Madolimov, F., Мадолимов, Ф., Madolimov, F.
Аңдатпа
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
Метадеректер дереккөзі: журналдың OAI-PMH архиві · Sindex толық мәтінді сақтамайды, дереккөзге сілтеме береді.