Version 1
: Received: 22 May 2024 / Approved: 23 May 2024 / Online: 23 May 2024 (07:42:02 CEST)
How to cite:
Saki, A.; Faghihi, U.; Baldé, I. Differentiating Gliosarcoma from Glioblastoma: A Novel Approach Using PEACE and XGBoost. Preprints2024, 2024051505. https://doi.org/10.20944/preprints202405.1505.v1
Saki, A.; Faghihi, U.; Baldé, I. Differentiating Gliosarcoma from Glioblastoma: A Novel Approach Using PEACE and XGBoost. Preprints 2024, 2024051505. https://doi.org/10.20944/preprints202405.1505.v1
Saki, A.; Faghihi, U.; Baldé, I. Differentiating Gliosarcoma from Glioblastoma: A Novel Approach Using PEACE and XGBoost. Preprints2024, 2024051505. https://doi.org/10.20944/preprints202405.1505.v1
APA Style
Saki, A., Faghihi, U., & Baldé, I. (2024). Differentiating Gliosarcoma from Glioblastoma: A Novel Approach Using PEACE and XGBoost. Preprints. https://doi.org/10.20944/preprints202405.1505.v1
Chicago/Turabian Style
Saki, A., Usef Faghihi and Ismaila Baldé. 2024 "Differentiating Gliosarcoma from Glioblastoma: A Novel Approach Using PEACE and XGBoost" Preprints. https://doi.org/10.20944/preprints202405.1505.v1
Abstract
In this paper, we introduce a new causal formula to distinguish Gliosarcoma (GSM) from Glioblastoma (GBM). Our formula combines Probabilistic Easy Variational Causal Effect (PEACE) with XGBoost, or eXtreme Gradient Boosting algorithm. Unlike prior research, which often relied on statistical models to reduce dataset dimensions before causal analysis, our approach uses the complete dataset with PEACE and XGBoost algorithm. PEACE provides a comprehensive measurement of direct causal effects, applicable to both continuous and discrete variables. It offers a spectrum of both positive and negative causal effects of the events causal effect values based on the degree ?, reflecting the rarity and frequency of the events. By using PEACE with XGBoost, we achieve a detailed and nuanced understanding of the causal relationships within the dataset features, facilitating accurate differentiation between GSM and GBM.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
