Ilim ha’m ja’miyet № 1-1 (2026) · Саҳифаҳои 24-26
HIGH-PRECISION AI PIPELINE EXPLAINED IN PROSTATA CANCER
Madolimov, F., Мадолимов, Ф., Madolimov, F.
Аннотатсия
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
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