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All Journal Infolitika Journal of Data Science
Isra Firmansyah
Department of Child Health, dr. Zainoel Abidin General Hospital, Banda Aceh 24415, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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Maternal Health Risk Detection Using Light Gradient Boosting Machine Approach Teuku Rizky Noviandy; Sarah Ika Nainggolan; Raihan Raihan; Isra Firmansyah; Rinaldi Idroes
Infolitika Journal of Data Science Vol. 1 No. 2 (2023): December 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v1i2.123

Abstract

Maternal health risk detection is crucial for reducing morbidity and mortality among pregnant women. In this study, we employed the Light Gradient Boosting Machine (LightGBM) model to identify risk levels using data from rural healthcare facilities. The dataset included key health indicators aligned with the United Nations Sustainable Development Goals. The LightGBM model underwent rigorous optimization through hyperparameter tuning and 10-fold cross-validation. Its predictive performance was benchmarked against other algorithms using accuracy, precision, recall, and F1-score, with feature importance assessed to identify critical risk predictors. The LightGBM model demonstrating the highest performance across all metrics. The results underscore the value of advanced machine learning techniques in public health. Future research directions include expanding the demographic scope, incorporating temporal data, and enhancing model transparency. This study highlights the transformative potential of machine learning in maternal healthcare, providing a foundation for improved risk detection and proactive healthcare interventions.
Co-Authors Raihan Raihan Rinaldi Idroes Sarah Ika Nainggolan Teuku Rizky Noviandy