A proposed tree-based explainable artificial intelligence approach for the prediction of angina pectoris

Guldogan, Emek; Yagin, Fatma Hilal; Pinar, Abdulvahap; Colak, Cemil; Kadry, Seifedine; Kim, Jungeun

doi:10.1038/s41598-023-49673-2

Cited by 5 publications

(2 citation statements)

References 53 publications

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“…The final prediction is a weighted combination of the individual weak learner predictions, with higher-performing weak learners having more influence. AdaBoost has proven effective in boosting the performance of various base classifiers, making it a valuable tool in the ensemble learning toolbox [ 24 , 25 ].…”

Section: Methodsmentioning

confidence: 99%

Assessment of Sepsis Risk at Admission to the Emergency Department: Clinical Interpretable Prediction Model

Aygun,

Yagin,

Yagin

et al. 2024

Diagnostics

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This study aims to develop an interpretable prediction model based on explainable artificial intelligence to predict bacterial sepsis and discover important biomarkers. A total of 1572 adult patients, 560 of whom were sepsis positive and 1012 of whom were negative, who were admitted to the emergency department with suspicion of sepsis, were examined. We investigated the performance characteristics of sepsis biomarkers alone and in combination for confirmed sepsis diagnosis using Sepsis-3 criteria. Three different tree-based algorithms—Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Adaptive Boosting (AdaBoost)—were used for sepsis prediction, and after examining comprehensive performance metrics, descriptions of the optimal model were obtained with the SHAP method. The XGBoost model achieved accuracy of 0.898 (0.868–0.929) and area under the ROC curve (AUC) of 0.940 (0.898–0.980) with a 95% confidence interval. The five biomarkers for predicting sepsis were age, respiratory rate, oxygen saturation, procalcitonin, and positive blood culture. SHAP results revealed that older age, higher respiratory rate, procalcitonin, neutrophil–lymphocyte count ratio, C-reactive protein, plaque, leukocyte particle concentration, as well as lower oxygen saturation, systolic blood pressure, and hemoglobin levels increased the risk of sepsis. As a result, the Explainable Artificial Intelligence (XAI)-based prediction model can guide clinicians in the early diagnosis and treatment of sepsis, providing more effective sepsis management and potentially reducing mortality rates and medical costs.

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Section: Methodsmentioning

confidence: 99%

Assessment of Sepsis Risk at Admission to the Emergency Department: Clinical Interpretable Prediction Model

Aygun,

Yagin,

Yagin

et al. 2024

Diagnostics

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“…EBM is built on the concept of boosting, where numerous weak models are joined to form a powerful and useful predictive model. It consecutively fits a sequence of decision trees/weak learners, each one dedicated to correcting the errors committed by the former models [6,7].…”

Section: Introductionmentioning

confidence: 99%

Combining the Strengths of the Explainable Boosting Machine and Metabolomics Approaches for Biomarker Discovery in Acute Myocardial Infarction

Arslan,

Yagin,

Algarni

et al. 2024

Diagnostics

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Acute Myocardial Infarction (AMI), a common disease that can have serious consequences, occurs when myocardial blood flow stops due to occlusion of the coronary artery. Early and accurate prediction of AMI is critical for rapid prognosis and improved patient outcomes. Metabolomics, the study of small molecules within biological systems, is an effective tool used to discover biomarkers associated with many diseases. This study intended to construct a predictive model for AMI utilizing metabolomics data and an explainable machine learning approach called Explainable Boosting Machines (EBM). The EBM model was trained on a dataset of 102 prognostic metabolites gathered from 99 individuals, including 34 healthy controls and 65 AMI patients. After a comprehensive data preprocessing, 21 metabolites were determined as the candidate predictors to predict AMI. The EBM model displayed satisfactory performance in predicting AMI, with various classification performance metrics. The model’s predictions were based on the combined effects of individual metabolites and their interactions. In this context, the results obtained in two different EBM modeling, including both only individual metabolite features and their interaction effects, were discussed. The most important predictors included creatinine, nicotinamide, and isocitrate. These metabolites are involved in different biological activities, such as energy metabolism, DNA repair, and cellular signaling. The results demonstrate the potential of the combination of metabolomics and the EBM model in constructing reliable and interpretable prediction outputs for AMI. The discussed metabolite biomarkers may assist in early diagnosis, risk assessment, and personalized treatment methods for AMI patients. This study successfully developed a pipeline incorporating extensive data preprocessing and the EBM model to identify potential metabolite biomarkers for predicting AMI. The EBM model, with its ability to incorporate interaction terms, demonstrated satisfactory classification performance and revealed significant metabolite interactions that could be valuable in assessing AMI risk. However, the results obtained from this study should be validated with studies to be carried out in larger and well-defined samples.

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High‐Detectivity All‐Polymer Photodiode Empowers Smart Vitality Surveillance and Computational Imaging Rivaling Silicon Diodes

Chandran,

Ma,

et al. 2024

Advanced Materials

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Near‐infrared (NIR) organic photodetectors (OPDs), particularly all‐polymer‐based ones, hold substantial commercial promise in the healthcare and imaging sectors. However, the process of optimizing their active layer composition to achieve highly competitive figures of merit lacks a clear direction and methodology. In this work, celebrity polymer acceptor PY‐IT into a more NIR absorbing host system PBDB‐T:PZF‐V, to significantly enhance the photodetection competence, is introduced. The refined all‐polymer ternary broadband photodetector demonstrates superior performance metrics, including experimentally measured noise current as low as 6 fA Hz−1/2, specific detectivity reaching 8 × 1012 Jones, linear dynamic range (LDR) of 145 dB, and swift response speed surpassing 200 kHz, striking a fair balance between sensitivity and response speed. Comprehensive morphological and photophysical characterizations elucidate the mechanisms behind the observed performance enhancements in this study, which include reduced trap density, enhanced charge transport, diminished charge recombination, and balanced electron/hole mobilities. Moreover, the practical deployment potential of the proof‐of‐concept device in self‐powered mode is demonstrated through their application in a machine learning‐based cuffless blood pressure (BP) estimation system and in high‐resolution computational imaging across complex environments, where they are found to quantitatively rival commercial silicon diodes.

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A proposed tree-based explainable artificial intelligence approach for the prediction of angina pectoris

Cited by 5 publications

References 53 publications

Assessment of Sepsis Risk at Admission to the Emergency Department: Clinical Interpretable Prediction Model

Assessment of Sepsis Risk at Admission to the Emergency Department: Clinical Interpretable Prediction Model

Combining the Strengths of the Explainable Boosting Machine and Metabolomics Approaches for Biomarker Discovery in Acute Myocardial Infarction

High‐Detectivity All‐Polymer Photodiode Empowers Smart Vitality Surveillance and Computational Imaging Rivaling Silicon Diodes

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