Classification of short single-lead electrocardiograms (ECGs) for atrial fibrillation detection using piecewise linear spline and XGBoost

Chen, Yao; Wang, Xiao; Jung, Yeon Sang; Abedi, Vida; Zand, Ramin; Bikak, Marvi; Adibuzzaman, Mohammad

doi:10.1088/1361-6579/aadf0f

Cited by 52 publications

(26 citation statements)

References 27 publications

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“…However, for the first time, this algorithm has been used to construct prediction models for osteosarcoma patient survival. As XGBoost is good at dealing with complex problems, it is suitable for most other types of complex classification problems [27][28][29] . www.nature.com/scientificreports/ Our study had some advantages.…”

Section: Discussionmentioning

confidence: 99%

Predictive model for the 5-year survival status of osteosarcoma patients based on the SEER database and XGBoost algorithm

Jiang

Pan

et al. 2021

Sci Rep

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Osteosarcoma is the most common bone malignancy, with the highest incidence in children and adolescents. Survival rate prediction is important for improving prognosis and planning therapy. However, there is still no prediction model with a high accuracy rate for osteosarcoma. Therefore, we aimed to construct an artificial intelligence (AI) model for predicting the 5-year survival of osteosarcoma patients by using extreme gradient boosting (XGBoost), a large-scale machine-learning algorithm. We identified cases of osteosarcoma in the Surveillance, Epidemiology, and End Results (SEER) Research Database and excluded substandard samples. The study population was 835 and was divided into the training set (n = 668) and validation set (n = 167). Characteristics selected via survival analyses were used to construct the model. Receiver operating characteristic (ROC) curve and decision curve analyses were performed to evaluate the prediction. The accuracy of the prediction model was excellent both in the training set (area under the ROC curve [AUC] = 0.977) and the validation set (AUC = 0.911). Decision curve analyses proved the model could be used to support clinical decisions. XGBoost is an effective algorithm for predicting 5-year survival of osteosarcoma patients. Our prediction model had excellent accuracy and is therefore useful in clinical settings.

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Section: Discussionmentioning

confidence: 99%

Predictive model for the 5-year survival status of osteosarcoma patients based on the SEER database and XGBoost algorithm

Jiang

Pan

et al. 2021

Sci Rep

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“…To develop predictive models for the daily incidence of OHCA, we used the the eXtreme Gradient Boosting (XGBoost) algorithm, which is an optimised distributed gradient boosting library widely used by data scientists for many ML challenges. [11][12][13] Hyperparameters of the XGBoost algorithm were chosen to maximise the predictive ability of the model using fourfold crossvalidation. In fourfold cross-validation, we classified our dataset into four groups, and the XGBoost algorithm fitted decision trees to three groups and used the remaining group for validation.…”

Section: Development Of Predictive Modelsmentioning

confidence: 99%

“…Machine learning (ML) has recently emerged as a novel approach to integrate multiple quantitative variables to improve diagnosis and accuracy of incidence predictions in cardiovascular medicine. [11][12][13] Since meteorological data are very extensive and complex, ML can help identify associations not identified by conventional one-dimensional statistical approaches. By combining OHCA data with high-resolution meteorological data, such as daily forecasts, ML could use advanced analytics to build a warning system for individuals potentially at risk for OHCA of cardiac origin through internet of things (IoT) devices.…”

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confidence: 99%

Machine learning model for predicting out-of-hospital cardiac arrests using meteorological and chronological data

Nakashima

Ogata

Noguchi³

et al. 2021

Heart

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ObjectivesTo evaluate a predictive model for robust estimation of daily out-of-hospital cardiac arrest (OHCA) incidence using a suite of machine learning (ML) approaches and high-resolution meteorological and chronological data.MethodsIn this population-based study, we combined an OHCA nationwide registry and high-resolution meteorological and chronological datasets from Japan. We developed a model to predict daily OHCA incidence with a training dataset for 2005–2013 using the eXtreme Gradient Boosting algorithm. A dataset for 2014–2015 was used to test the predictive model. The main outcome was the accuracy of the predictive model for the number of daily OHCA events, based on mean absolute error (MAE) and mean absolute percentage error (MAPE). In general, a model with MAPE less than 10% is considered highly accurate.ResultsAmong the 1 299 784 OHCA cases, 661 052 OHCA cases of cardiac origin (525 374 cases in the training dataset on which fourfold cross-validation was performed and 135 678 cases in the testing dataset) were included in the analysis. Compared with the ML models using meteorological or chronological variables alone, the ML model with combined meteorological and chronological variables had the highest predictive accuracy in the training (MAE 1.314 and MAPE 7.007%) and testing datasets (MAE 1.547 and MAPE 7.788%). Sunday, Monday, holiday, winter, low ambient temperature and large interday or intraday temperature difference were more strongly associated with OHCA incidence than other the meteorological and chronological variables.ConclusionsA ML predictive model using comprehensive daily meteorological and chronological data allows for highly precise estimates of OHCA incidence.

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“…It works well even on small datasets (where it outperforms deep learning approaches), it is robust to outliers and it is able to model complex interdependencies. For these reasons, it has been used by many researchers in various biomedical fields, e.g., [49][50][51], etc.…”

Section: Discussionmentioning

confidence: 99%

Changes in Phonation and Their Relations with Progress of Parkinson’s Disease

et al. 2018

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Hypokinetic dysarthria, which is associated with Parkinson's disease (PD), affects several speech dimensions, including phonation. Although the scientific community has dealt with a quantitative analysis of phonation in PD patients, a complex research revealing probable relations between phonatory features and progress of PD is missing. Therefore, the aim of this study is to explore these relations and model them mathematically to be able to estimate progress of PD during a two-year follow-up. We enrolled 51 PD patients who were assessed by three commonly used clinical scales. In addition, we quantified eight possible phonatory disorders in five vowels. To identify the relationship between baseline phonatory features and changes in clinical scores, we performed a partial correlation analysis. Finally, we trained XGBoost models to predict the changes in clinical scores during a two-year follow-up. For two years, the patients' voices became more aperiodic with increased microperturbations of frequency and amplitude. Next, the XGBoost models were able to predict changes in clinical scores with an error in range 11-26%. Although we identified some significant correlations between changes in phonatory features and clinical scores, they are less interpretable. This study suggests that it is possible to predict the progress of PD based on the acoustic analysis of phonation. Moreover, it recommends utilizing the sustained vowel /i/ instead of /a/.

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Classification of short single-lead electrocardiograms (ECGs) for atrial fibrillation detection using piecewise linear spline and XGBoost

Abstract: Our algorithm presents a good performance on multi-label short ECG classification with selected morphological features.

Cited by 52 publications

References 27 publications

Predictive model for the 5-year survival status of osteosarcoma patients based on the SEER database and XGBoost algorithm

Predictive model for the 5-year survival status of osteosarcoma patients based on the SEER database and XGBoost algorithm

Machine learning model for predicting out-of-hospital cardiac arrests using meteorological and chronological data

Changes in Phonation and Their Relations with Progress of Parkinson’s Disease

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