XGBoost-Based Simple Three-Item Model Accurately Predicts Outcomes of Acute Ischemic Stroke

Chung, Chen-Chih; Su, Emily Chia Yu; Chen, Jia Hung; Chen, Yi-Tui; Kuo, Chao-Yang

doi:10.3390/diagnostics13050842

Cited by 10 publications

(6 citation statements)

References 52 publications

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“…Third, it is recommended to compare the predictive model with another prognostic system, such as the Acute Stroke Registry and Analysis of Lausanne score, to provide a more comprehensive evaluation and enhance the persuasiveness of the findings 44 . Finally, it should be noted that the NIHSS score, which stands for National Institutes of Health Stroke Scale, is a widely predictor of outcomes in AIS predictive models 45 . The NIHSS score upon admission was found to be a significant factor in the development of unfavorable outcomes.…”

Section: Discussionmentioning

confidence: 99%

Development and validation of a machine learning-based prognostic risk stratification model for acute ischemic stroke

Wang,

Hong,

Liu

et al. 2023

Sci Rep

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Acute ischemic stroke (AIS) is a most prevalent cause of serious long-term disability worldwide. Accurate prediction of stroke prognosis is highly valuable for effective intervention and treatment. As such, the present retrospective study aims to provide a reliable machine learning-based model for prognosis prediction in AIS patients. Data from AIS patients were collected retrospectively from the Second Affiliated Hospital of Xuzhou Medical University between August 2017 and July 2019. Independent prognostic factors were identified by univariate and multivariate logistic analysis and used to develop machine learning (ML) models. The ML model performance was assessed by area under the receiver operating characteristic curve (AUC) and radar plot. Shapley Additive explanations (SHAP) values were used to interpret the importance of all features included in the predictive model. A total of 677 AIS patients were included in the present study. Poor prognosis was observed in 209 patients (30.9%). Six variables, including neuron specific enolase (NSE), homocysteine (HCY), S-100β, dysphagia, C-reactive protein (CRP), and anticoagulation were included to establish ML models. Six different ML algorithms were tested, and Random Forest model was selected as the final predictive model with the greatest AUC of 0.908. Moreover, according to SHAP results, NSE impacted the predictive model the most, followed by HCY, S-100β, dysphagia, CRP and anticoagulation. Based on the RF model, an online tool was constructed to predict the prognosis of AIS patients and assist clinicians in optimizing patient treatment. The present study revealed that NSE, HCY, CRP, S-100β, anticoagulation, and dysphagia were important factors for poor prognosis in AIS patients. ML algorithms were used to develop predictive models for predicting the prognosis of AIS patients, with the RF model presenting the optimal performance.

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Section: Discussionmentioning

confidence: 99%

Development and validation of a machine learning-based prognostic risk stratification model for acute ischemic stroke

Wang,

Hong,

Liu

et al. 2023

Sci Rep

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“…Extreme Gradient Boosting (XGBoost): XGBoost well implements the gradient augmentation approach [29] (see Figure 10). The gradient gain alternative may be rigorously developed for precision and optimization, even if no mathematical breakthroughs exist in this specific instance.…”

Section: ) the Baseline Modelsmentioning

confidence: 99%

An Improved Concatenation of Deep Learning Models for Predicting and Interpreting Ischemic Stroke

Sakri,

Basheer,

Zain

et al. 2024

IEEE Access

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Early detection of stroke warning symptoms can help reduce the severity of ischemic stroke, the leading cause of mortality and disability worldwide. This study aims to develop a model to predict the disease by leveraging machine learning-based models. A model that concatenates a convolutional neural network and a long short-term memory was developed as the proposed model. Seven other classifiers were treated as the baseline models: logistic regression, random forest, extreme gradient boosting, k-nearest neighbor, artificial neural network, long short-term memory, and convolutional neural network. All models were trained using a healthcare dataset of 5110 patients' health profiles. A synthetic minority oversampling technique was deployed to balance the data. Metrics such as accuracy, precision, F1-score, recall, area under the curve, and confusion metrics were used to evaluate the models' performance. With a 95.9% accuracy, the proposed model outperformed the models employed in this study and improved the accuracy of prior studies that used the same dataset. The Shapley Additive Explanations method was applied to explain the result obtained by the best model. The proposed model was created to predict ischemic stroke. It considers each patient's profile, allowing for personalized decision-making in resource-constrained settings.INDEX TERMS Ischemic stroke prediction, SHAP method, hybrid deep learning model, machine learning

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“…where F is the ensemble space of CART. To learn the set of functions, a regularization objective is typically included to minimize the objective function [49]:…”

Section: Xgboostmentioning

confidence: 99%

Rapeseed Seed Coat Color Classification Based on the Visibility Graph Algorithm and Hyperspectral Technique

Zou,

Zhu,

Wang

et al. 2024

Agronomy

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Information technology and statistical modeling have made significant contributions to smart agriculture. Machine vision and hyperspectral technologies, with their non-destructive and real-time capabilities, have been extensively utilized in the non-destructive diagnosis and quality monitoring of crops and seeds, becoming essential tools in traditional agriculture. This work applies these techniques to address the color classification of rapeseed, which is of great significance in the field of rapeseed growth diagnosis research. To bridge the gap between machine vision and hyperspectral technology, a framework is developed that includes seed color calibration, spectral feature extraction and fusion, and the recognition modeling of three seed colors using four machine learning methods. Three categories of rapeseed coat colors are calibrated based on visual perception and vector-square distance methods. A fast-weighted visibility graph method is employed to map the spectral reflectance sequences to complex networks, and five global network attributes are extracted to fuse the full-band reflectance as model input. The experimental results demonstrate that the classification recognition rate of the fused feature reaches 0.943 under the XGBoost model, confirming the effectiveness of the network features as a complement to the spectral reflectance. The high recognition accuracy and simple operation process of the framework support the further application of hyperspectral technology to analyze the quality of rapeseed.

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XGBoost-Based Simple Three-Item Model Accurately Predicts Outcomes of Acute Ischemic Stroke

Cited by 10 publications

References 52 publications

Development and validation of a machine learning-based prognostic risk stratification model for acute ischemic stroke

Development and validation of a machine learning-based prognostic risk stratification model for acute ischemic stroke

An Improved Concatenation of Deep Learning Models for Predicting and Interpreting Ischemic Stroke

Rapeseed Seed Coat Color Classification Based on the Visibility Graph Algorithm and Hyperspectral Technique

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