Classification of Liver Cirrhosis Stages Using Gradient Boosting Classifier and Stratified K-Fold Cross Validation

Purwakaning Purnomo Agung, Paul L Tahalele

Sari


Sirosis hati merupakan manifestasi fibrosis lanjut yang berkontribusi signifikan terhadap mortalitas global. Stratifikasi stadium secara dini melalui intervensi komputasional menjadi krusial dalam menjustifikasi keputusan klinis secara non-invasif. Penelitian ini mengeksplorasi pengembangan model klasifikasi stadium sirosis menggunakan Gradient Boosting Classifier (GBC) yang diintegrasikan dengan Stratified K-Fold Cross Validation. Dataset bersumber dari uji klinis Primary Biliary Cirrhosis (PBC) Mayo Clinic yang mencakup 418 rekam medis dan 16 fitur klinis. Protokol penelitian meliputi imputasi statistik, analisis fitur, dan binarisasi target antara stadium lanjut (Stage 4) serta kategori non-lanjut. Performa GBC dibandingkan dengan Logistic Regression dan Random Forest melalui skema validasi yang ketat. Hasil menunjukkan efektivitas GBC dengan raihan akurasi 93,55% dan AUC 0,9974, melampaui performa berbagai kerangka kerja deep learning dan ensemble terbaru dalam literatur. Analisis feature importance mengonfirmasi bahwa kadar Bilirubin, Copper, Albumin, dan Prothrombin merupakan prediktor dominan dalam menentukan progresivitas penyakit. Integrasi machine learning berbasis biomarker klinis ini terbukti mampu memberikan dukungan diagnostik dengan fidelitas tinggi serta menawarkan stabilitas prediksi yang lebih unggul bagi manajemen klinis sirosis hati.

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DOI: http://dx.doi.org/10.30811/jaise.v6i2.9157

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