Yilmaz, R.; Yagin, F.H.; Colak, C.; Toprak, K.; Samee, N.A.; Mahmoud, N.F. Analysis of Hematological Predictors via Explainable Artificial Intelligence in Prediction of Acute Heart Failure: A Retrospective Study. Preprints2023, 2023071156. https://doi.org/10.20944/preprints202307.1156.v1
APA Style
Yilmaz, R., Yagin, F.H., Colak, C., Toprak, K., Samee, N.A., & Mahmoud, N.F. (2023). Analysis of Hematological Predictors via Explainable Artificial Intelligence in Prediction of Acute Heart Failure: A Retrospective Study. Preprints. https://doi.org/10.20944/preprints202307.1156.v1
Chicago/Turabian Style
Yilmaz, R., Nagwan Abdel Samee and Noha F. Mahmoud. 2023 "Analysis of Hematological Predictors via Explainable Artificial Intelligence in Prediction of Acute Heart Failure: A Retrospective Study" Preprints. https://doi.org/10.20944/preprints202307.1156.v1
Abstract
Background: Acute heart failure (AHF) is a serious medical problem that necessitates hospitalisation and often results in death. Patients hospitalised to the emergency department (ED) should therefore receive an immediate diagnosis and treatment. Unfortunately, there is not yet a fast and accurate laboratory test for identifying AHF. The purpose of this research is to apply the principles of explainable artificial intelligence (XAI) to the analysis of hematological predictors for AHF. Methods: In this retrospective analysis, 425 patients with AHF and 430 healthy individuals served as assessments. Patients' demographic and hematological information was analyzed to determine AHF. Important risk variables for AHF diagnosis were identified using LASSO feature selection. To test the efficacy of the suggested prediction model (XGBoost), a 10-fold cross-validation procedure was implemented. The area under the receiver operating characteristic curve (AUC), F1 score, Brier score, and Positive Predictive Value (PPV) and Negative Predictive Value (NPV) were all computed to evaluate the model's efficacy. Permutation-based analysis and SHAP, were used to assess the importance and influence of the model's incorporated risk factors. Results: White blood cell (WBC), monocytes, neutrophils, neutrophil-lymphocyte ratio (NLR), red cell distribution width-standard deviation (RDW-SD), RDW-coefficient of variation (RDW-CV), and platelet distribution width (PDW) values were significantly higher than the healthy group (p<0.05). On the other hand, erythrocyte, hemoglobin, basophil, lymphocyte, mean platelet volume (MPV), platelet, hematocrit, mean erythrocyte hemoglobin (MCH) and procalcitonin (PCT) values were found to be significantly lower in AHF patients compared to healthy controls (p <0.05). When XGBoost was used in conjunction with LASSO to estimate AHF, the resulting model had an AUC of 87.9%, an F1 score of 87.4%, a Brier score of 0.036, and an F1 score of 87.4%. PDW, age, RDW-SD, and PLT were identified as the most crucial risk factors in differentiating AHF. Conclusions: The XGBoost model demonstrated exceptional performance in accurately estimating Acute Heart Failure, and the application of Explainable Artificial Intelligence effectively provided intuitive explanations for the model's estimations. The suggested interpretable model holds potential for the identification of patients at high risk, thereby facilitating the optimization of treatment and planning for follow-up in cases of AHF.
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.
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