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

Chindukuru Naga Sai Sreedhar () and Loganayagi S ()

SPAST Reports, 2024, vol. 1, issue 3

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 fromPhysioNet/Computing in Cardiology Challenge 2019's training set. The G power software informed thesample 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, theLightGBM Technique achieved 96.41% accuracy, surpassing the AdaBoost Classifier's 77.58%. A significantdifference was observed between the two, evidenced by a P value of 0.019. In conclusion, the Light GradientBoosting Machine classifier offers superior accuracy in predicting sepsis events

Keywords: Machine Learning; Adaboost Classifier; Innovative Novel LightGBM Technique (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
https://spast.org/article/view/4919/302 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bps:jspath:v:1:y:2024:i:3:id:4919

Access Statistics for this article

SPAST Reports is currently edited by Srinesh Singh Thakur

More articles in SPAST Reports from SPAST Foundation
Bibliographic data for series maintained by Srinesh Singh Thakur ().