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Sains Malaysiana 55(4)(2026): 756-773
http://doi.org/10.17576/jsm-2026-5504-14
Bayesian
Logistic Regression to Explore the Role of Complete Blood Count in Kidney
Disease Mortality
(Regresi Logistik Bayesian untuk Meneroka Peranan Kiraan Darah Lengkap dalam Kematian Penyakit Ginjal)
DG SITI
NURISYA SAHIRAH AG ISHA1, NURLIYANA JUHAN2,*,
YONG ZULINA ZUBAIRI3, NORNAZIRAH AZIZAN4, CHONG MUN HO1,
ABU SAYED MD. AL MAMUN5 & QIN ZHI LEE6
1Faculty
of Science and Technology, Universiti Malaysia Sabah,
Jalan UMS, 88400 Kota Kinabalu, Sabah, Malaysia
2Preparatory
Centre for Science and Technology, Universiti Malaysia Sabah, Jalan UMS, 88400 Kota Kinabalu, Sabah, Malaysia
3Institute
for Advanced Studies, University of Malaya, 50603 Kuala Lumpur, Malaysia
4Faculty
of Medicine and Health Sciences, Universiti Malaysia
Sabah, Jalan UMS, 88400 Kota Kinabalu, Sabah, Malaysia
5Department
of Statistics, University of Rajshahi, Rajshahi, Bangladesh
6Queen
Elizabeth Hospital, Locked Bag No. 2029, 88586 Kota Kinabalu, Sabah, Malaysia
Received:
4 July 2025/Accepted: 20 April 2026
Abstract
Kidney disease is a major global health
challenge, ranking fifth in Malaysia and ninth worldwide as a leading cause of
death in 2021. This growing burden highlights the need for cost-effective tools
to support early identification of patients at risk of mortality. The complete
blood count (CBC) is an affordable, widely used diagnostic test, while Bayesian
methods offer advantages for incorporating prior knowledge and quantifying
uncertainty. However, the use of CBC parameters with Bayesian approaches for
mortality prediction among kidney disease patients in Malaysia remains limited.
This study aimed to develop a risk stratification model for kidney disease
mortality using CBC data and Bayesian logistic regression (BLR). A
retrospective study was conducted using data from 5,158 patients with kidney
disease treated at Queen Elizabeth I Hospital. The final multivariate BLR model
identified 13 significant predictors of mortality. The strongest predictors
were low haemoglobin, high mean platelet volume (MPV), and high
neutrophil-to-lymphocyte ratio (NLR), followed by high white blood cells (WBC),
and hospitalisation history. The model demonstrated good calibration and
discrimination, with an area under the receiver operating characteristic curve
(AUROC) and area under the precision-recall curve (AUPRC) greater than 0.8,
supporting its reliability for mortality risk stratification. These findings
suggest that combining CBC parameters with demographic information may improve
early detection and clinical decision-making, particularly in resource-limited
settings.
Keywords: Bayesian logistic regression; complete
blood count; kidney disease; mortality; risk stratification
Abstrak
Penyakit buah pinggang merupakan cabaran kesihatan global yang utama, menduduki
tempat kelima di Malaysia dan kesembilan di peringkat dunia sebagai penyebab
utama kematian pada tahun 2021. Beban yang semakin meningkat ini menekankan
keperluan terhadap alat diagnostik yang kos efektif untuk memudahkan pengesanan
awal. Kiraan darah lengkap (KDL) merupakan ujian diagnostik yang berpatutan dan
digunakan secara rutin, manakala kaedah Bayesian menawarkan kelebihan dengan
menggabungkan pengetahuan terdahulu dan memberikan pengkuantitian ketakpastian
yang lebih tepat. Walau bagaimanapun, penggunaan parameter KDL dan Bayesian bagi
meramal kematian dalam kalangan pesakit buah pinggang di Malaysia masih terhad.
Kajian ini bertujuan untuk membangunkan model stratifikasi risiko bagi kematian
akibat penyakit buah pinggang menggunakan KDL dan regresi logistik Bayesian
(BLR). Kajian retrospektif ini menganalisis data daripada 5,158 pesakit buah
pinggang dari Hospital Queen Elizabeth I. Model BLR multivariat akhir mengenal
pasti 13 peramal kematian yang signifikan. Peramal yang paling kuat ialah hemoglobin rendah, min isi padu platelet (MPV) yang tinggi dan nisbah neutrofil kepada limfosit (NLR) yang tinggi, diikuti oleh
sel darah putih (WBC) yang tinggi dan sejarah kemasukan ke hospital. Model
menunjukkan prestasi penentukuran dan diskriminasi yang baik, dengan kawasan
bawah lengkungan dalam menerima ciri operasi (AUROC) dan kawasan di bawah
lengkung penarikan balik ketepatan (AUPRC) melebihi 0.8, sekali gus menyokong
kebolehpercayaannya untuk stratifikasi risiko kematian. Penemuan ini mencadangkan
bahawa penggabungan parameter KDL dengan maklumat demografi berpotensi meningkatkan
pengesanan awal pesakit berisiko dan keputusan klinikal, khususnya dalam
persekitaran dengan sumber terhad.
Kata kunci: Bayesian regresi logistik; kiraan darah lengkap; kematian; penyakit buah pinggang; stratifikasi risiko
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*Corresponding author; email:
liyana87@ums.edu.my
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