Assessing LightGBM Performance in Automated Leukemia Cell Classification

Rara Syifa Qaisa; Hayatun Maghfirah; Suryadi Suryadi; Noviana  Husdayanti; Rivansyah Suhendra

doi:10.60084/ijds.v4i1.351

Authors

Rara Syifa Qaisa Department of Information Technology, Faculty of Engineering, Universitas Teuku Umar, Aceh Barat 23681, Indonesia
Hayatun Maghfirah Department of Information Technology, Faculty of Engineering, Universitas Teuku Umar, Aceh Barat 23681, Indonesia
Suryadi Suryadi Department of Information Technology, Faculty of Engineering, Universitas Teuku Umar, Aceh Barat 23681, Indonesia
Noviana Husdayanti Teuku Umar Hospital, Aceh Jaya 23654, Indonesia
Rivansyah Suhendra Department of Information Technology, Faculty of Engineering, Universitas Teuku Umar, Aceh Barat 23681, Indonesia

DOI:

https://doi.org/10.60084/ijds.v4i1.351

Keywords:

LightGBM, Leukemia, Image classification, Machine learning, Blood cell subtypes

Abstract

Leukemia is a type of blood cancer that requires fast and accurate diagnosis for effective treatment. Manual identification of leukemia blood cell subtypes is often challenging, time-consuming, and prone to observer variability, making automated image-based classification essential. This study evaluates the performance of the Light Gradient-Boosting Machine (LightGBM) as a computationally efficient and interpretable alternative to deep learning models for classifying leukemia subtypes. The dataset includes 3,000 microscopic images representing five classes: acute lymphocytic, acute myelogenous, chronic lymphocytic, chronic myelogenous, and healthy blood cells. Images were preprocessed using bilinear interpolation to balance quality and efficiency, and 90 statistical features were extracted across 13 distinct color spaces. The model was trained on an 80% subset and validated on a 20% hold-out set after hyperparameter optimization. LightGBM achieved robust performance with an accuracy of 93.3%, precision of 99.1%, recall of 94.9%, and an F-measure of 96.8%. Feature importance analysis revealed that texture variance in the YIQ color space (STD_YIQ_I) was the most critical predictor, highlighting the biological relevance of chromatin texture in classification. These results indicate that LightGBM is an effective, lightweight, and reliable approach for leukemia subtype classification, holding strong potential for implementation in resource-constrained automated diagnostic systems.

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