Klasifikasi Diagnosis HCV di Mesir Berbasis Biomarker Menggunakan Regresi Logistik Multinomial
DOI:
https://doi.org/10.29313/bcss.v6i1.23395Keywords:
Hepatitis C Virus, Biomarker Klinis, Regresi Logistik MultinomialAbstract
Abstract. Hepatitis C Virus (HCV) infection is a major public health concern that can progress to liver fibrosis and cirrhosis if not properly managed. This study aims to identify the effects of clinical biomarkers and demographic factors on HCV diagnosis categories and to evaluate the performance of a statistical classification model. A quantitative approach was employed using secondary data from the UCI Machine Learning Repository, consisting of 615 patients as research subjects. The research procedures included data preprocessing, variable standardization, and multinomial logistic regression modeling. The results show distinct variations in biomarker distributions across diagnosis categories. Albumin and cholesterol levels tended to be higher in the blood donor group, while bilirubin and alkaline phosphatase levels were elevated among patients with fibrosis and cirrhosis. Age also played an important role in differentiating diagnoses. The model demonstrated satisfactory classification performance, although the suspect blood donor group exhibited higher rates of misclassification due to intermediate clinical characteristics. Overall, this study highlights the potential use of basic clinical laboratory data to support risk assessment and provides insights into the progression of HCV-related liver disease.
Abstrak. Infeksi Hepatitis C Virus (HCV) merupakan salah satu permasalahan kesehatan yang dapat berkembang menjadi fibrosis maupun sirosis hati apabila tidak ditangani secara tepat. Penelitian ini dilakukan untuk mengidentifikasi pengaruh biomarker klinis dan faktor demografis terhadap kategori diagnosis HCV, serta menilai kemampuan model statistik dalam mengklasifikasikan kondisi klinis pasien. Penelitian menggunakan pendekatan kuantitatif dengan data sekunder yang diperoleh dari UCI Machine Learning Repository, terdiri dari 615 pasien sebagai objek penelitian. Tahapan penelitian meliputi preprocessing data, standarisasi variabel numerik, dan pemodelan menggunakan regresi logistik multinomial. Hasil penelitian menunjukkan adanya perbedaan distribusi biomarker klinis antar kategori diagnosis, di mana Albumin dan Cholesterol cenderung lebih tinggi pada kelompok donor darah, sedangkan Bilirubin dan Alkaline Phosphatase meningkat pada kelompok fibrosis dan sirosis. Faktor usia juga berperan dalam membedakan diagnosis. Model dapat memprediksi kategori diagnosis dengan performa yang memadai, meskipun kelas suspect blood donor menunjukkan misklasifikasi yang lebih tinggi. Secara keseluruhan, penelitian ini mendukung pemanfaatan data klinis sederhana dalam membantu proses analisis risiko penyakit hati dan memberikan insight mengenai progresivitas HCV.
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