HIGH-PRECISION AI PIPELINE EXPLAINED IN PROSTATA CANCER

Madolimov, F., Мадолимов, Ф., Madolimov, F.

Ilim ha’m ja’miyet · 2026-yil

Annotatsiya

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.

Maqola ma’lumotlari
MualliflarMadolimov, F., Мадолимов, Ф., Madolimov, F.
JurnalIlim ha’m ja’miyet
Nashr sanasi2026-02-16
Son1-1
Betlar24-26
Tiluz_Latn

Kalit so‘zlar

prostata saratoni, mashinaviy o‘rganish, feature engineering, PCA, SHAP, diagnostika, рак предстательной железы, машинное обучение, инженерия признаков, PCA, SHAP, диагностика, prostate cancer, machine learning, feature engineering, PCA, SHAP, diagnostics

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