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All Journal Jurnal Informatika dan Teknik Elektro Terapan ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Andalasian International Journal of Applied Science, Engineering, and Technology Jurnal Andalas: Rekayasa dan Penerapan Teknologi
Ramadhamy, Queen Hesti
Universitas Andalas
Civil Engineering, Building, Construction & Architecture Computer Science & IT Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Mechanical Engineering Other

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COMPARISON OF GAUSSIAN NAIVE BAYES AND RANDOM FOREST FOR ANEMIA CLASSIFICATION USING HEMATOLOGICAL PARAMETERS baik budi; Refki Budiman; Queen Hesti Ramadhamy
Jurnal Informatika dan Teknik Elektro Terapan Vol. 14 No. 3 (2026)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v14i3.11019

Abstract

Anemia is a global health problem affecting approximately 1.92 billion people, or 24% of the population, according to the WHO. Accurate early detection is crucial for data-driven healthcare. This study evaluates two machine learning algorithms, Gaussian Naive Bayes (GNB) and Random Forest (RF). The classification is based on four key hematological parameters: Hemoglobin (Hb), Mean Corpuscular Volume (MCV), Mean Corpuscular Hemoglobin (MCH), and Mean Corpuscular Hemoglobin Concentration (MCHC). GNB relies on Bayes' Theorem with Gaussian distribution assumptions, whereas RF is a decision-tree-based ensemble method capable of capturing non-linear patterns without specific distributional assumptions. Evaluated using 5-fold cross-validation and standard metrics (accuracy, precision, recall, F1-score), results showed that RF outperformed GNB. RF achieved 94.2% accuracy (CV 94.9% ± 1.1%), compared to GNB's 90.5% (CV 90.1% ± 1.3%). RF feature importance confirmed Hb as the dominant predictor (score 0.562), aligning with its strong correlation to anemia (r = −0.80). Although not surpassing larger-scale studies, these results remain highly competitive. Ultimately, this research provides evidence to support the development of automated, data-driven clinical decision support systems for anemia detection.
Co-Authors Amirul Luthfi ANNISA MAULIDYA Antoni, Jefri Arya Witama, Reivan Baharuddin Baharuddin Baik Budi Baik Budi Budi, Baik Budiman, Refki Hafizh Qisthi Bakri Hanalde Andre Herier, Gilang Ramadhan Ikhwana Elfitri Ilhamdi Rusydi, Muhammad Imil Hamda Imran Jhonny Faizal Micko Tomas Muhammad Arya Diwa Pratama, Rizki Wahyu Primas Emeraldi Putra, Rangga Refki Budiman Refki Budiman Riko Nofendra Rudy Fernandez ZUMAR AHMAD