Prediction of Sepsis Using Light Gradient-Boosting Machine Classifier in Comparison with Adaboost Classifier Based on Accuracy

Chindukuru Sreedhar, Loganayagi S.

2023

Abstract

This study introduces a method to forecast sepsis employing the innovative LightGBM classifier model, juxtaposing its improved accuracy against the Adaboost Classifier model. The dataset was sourced from PhysioNet/Computing in Cardiology Challenge 2019’s training set. The G power software informed the sample size decision, suggesting 10 participants for each group, adopting a pretest power of 80%. A 95% confidence interval was applied, and a significance level was established at 0.05%. Remarkably, the LightGBM Technique achieved 96.41% accuracy, surpassing the AdaBoost Classifier’s 77.58%. A significant difference was observed between the two, evidenced by a P value of 0.019. In conclusion, the Light Gradient-Boosting Machine classifier offers superior accuracy in predicting sepsis events.

Paper Citation

in Harvard Style

Sreedhar C. and S. L. (2023). Prediction of Sepsis Using Light Gradient-Boosting Machine Classifier in Comparison with Adaboost Classifier Based on Accuracy. In Proceedings of the 1st International Conference on Artificial Intelligence for Internet of Things: Accelerating Innovation in Industry and Consumer Electronics - Volume 1: AI4IoT; ISBN 978-989-758-661-3, SciTePress, pages 595-600. DOI: 10.5220/0012570200003739

in Bibtex Style

@conference{ai4iot23,
author={Chindukuru Sreedhar and Loganayagi S.},
title={Prediction of Sepsis Using Light Gradient-Boosting Machine Classifier in Comparison with Adaboost Classifier Based on Accuracy},
booktitle={Proceedings of the 1st International Conference on Artificial Intelligence for Internet of Things: Accelerating Innovation in Industry and Consumer Electronics - Volume 1: AI4IoT},
year={2023},
pages={595-600},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012570200003739},
isbn={978-989-758-661-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Artificial Intelligence for Internet of Things: Accelerating Innovation in Industry and Consumer Electronics - Volume 1: AI4IoT
TI - Prediction of Sepsis Using Light Gradient-Boosting Machine Classifier in Comparison with Adaboost Classifier Based on Accuracy
SN - 978-989-758-661-3
AU - Sreedhar C.
AU - S. L.
PY - 2023
SP - 595
EP - 600
DO - 10.5220/0012570200003739
PB - SciTePress