A feature learning approach based on XGBoost for driving assessment and risk prediction

Shi, Xihang; Wong, Yiik Diew; Li, Michael Zhi-Feng; Palanisamy, Chandrasekar; Chai, Chunlei

doi:10.1016/j.aap.2019.05.005

Cited by 182 publications

(70 citation statements)

References 21 publications

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“…As a result, a nonlinear method was needed to assess the contribution of correlated factors on the spread of COVID-19 in China. Machine learning methods are good for solving nonlinear problems, and we used XGBoost [ 28 , 30 ] to create a nonlinear regression tree model for this study. Important factors were selected based on the cross-validation procedure in the XGBoost framework.…”

Section: Resultsmentioning

confidence: 99%

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Impact of Systematic Factors on the Outbreak Outcomes of the Novel COVID-19 Disease in China: Factor Analysis Study

Cao¹,

Tang²,

Zhang³

et al. 2020

J Med Internet Res

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Background The novel COVID-19 disease has spread worldwide, resulting in a new pandemic. The Chinese government implemented strong intervention measures in the early stage of the epidemic, including strict travel bans and social distancing policies. Prioritizing the analysis of different contributing factors to outbreak outcomes is important for the precise prevention and control of infectious diseases. We proposed a novel framework for resolving this issue and applied it to data from China. Objective This study aimed to systematically identify national-level and city-level contributing factors to the control of COVID-19 in China. Methods Daily COVID-19 case data and related multidimensional data, including travel-related, medical, socioeconomic, environmental, and influenza-like illness factors, from 343 cities in China were collected. A correlation analysis and interpretable machine learning algorithm were used to evaluate the quantitative contribution of factors to new cases and COVID-19 growth rates during the epidemic period (ie, January 17 to February 29, 2020). Results Many factors correlated with the spread of COVID-19 in China. Travel-related population movement was the main contributing factor for new cases and COVID-19 growth rates in China, and its contributions were as high as 77% and 41%, respectively. There was a clear lag effect for travel-related factors (previous vs current week: new cases, 45% vs 32%; COVID-19 growth rates, 21% vs 20%). Travel from non-Wuhan regions was the single factor with the most significant impact on COVID-19 growth rates (contribution: new cases, 12%; COVID-19 growth rate, 26%), and its contribution could not be ignored. City flow, a measure of outbreak control strength, contributed 16% and 7% to new cases and COVID-19 growth rates, respectively. Socioeconomic factors also played important roles in COVID-19 growth rates in China (contribution, 28%). Other factors, including medical, environmental, and influenza-like illness factors, also contributed to new cases and COVID-19 growth rates in China. Based on our analysis of individual cities, compared to Beijing, population flow from Wuhan and internal flow within Wenzhou were driving factors for increasing the number of new cases in Wenzhou. For Chongqing, the main contributing factor for new cases was population flow from Hubei, beyond Wuhan. The high COVID-19 growth rates in Wenzhou were driven by population-related factors. Conclusions Many factors contributed to the COVID-19 outbreak outcomes in China. The differential effects of various factors, including specific city-level factors, emphasize the importance of precise, targeted strategies for controlling the COVID-19 outbreak and future infectious disease outbreaks.

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Section: Resultsmentioning

confidence: 99%

“…Finally, the importance of each selected factor was determined based on the number of times the factor was split by the tree model, which was determined by XGBoost. For each week, a contribution percentage was calculated for each factor based on its relative importance [ 30 ].…”

Section: Methodsmentioning

confidence: 99%

Impact of Systematic Factors on the Outbreak Outcomes of the Novel COVID-19 Disease in China: Factor Analysis Study

Cao¹,

Tang²,

Zhang³

et al. 2020

J Med Internet Res

View full text Add to dashboard Cite

show abstract

“…The variable importance of the XGB model was also reported. The XGB models were ranked by variable importance on the gain, which implies the relative contribution of the corresponding variable to the model calculated by taking each variable's contribution for each tree in the model [43]. In addition, partial dependence plots were used to determine the marginal effect of features on the predicted outcome in the XGB model.…”

Section: Discussionmentioning

confidence: 99%

Emergency department routine data and the diagnosis of acute ischemic heart disease in patients with atypical chest pain

Kim

Park

et al. 2020

PLoS ONE

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Background Due to an aging population and the increasing proportion of patients with various comorbidities, the number of patients with acute ischemic heart disease (AIHD) who present to the emergency department (ED) with atypical chest pain is increasing. The aim of this study was to develop and validate a prediction model for AIHD in patients with atypical chest pain. Methods and results A chest pain workup registry, ED administrative database, and clinical data warehouse database were analyzed and integrated by using nonidentifiable key factors to create a comprehensive clinical dataset in a single academic ED from 2014 to 2018. Demographic findings, vital signs, and routine laboratory test results were assessed for their ability to predict AIHD. An extreme gradient boosting (XGB) model was developed and evaluated, and its performance was compared to that of a single-variable model and logistic regression model. The area under the receiver operating characteristic curve (AUROC) was calculated to assess discrimination. A calibration plot and partial dependence plots were also used in the analyses. Overall, 4,978 patients were analyzed. Of the 3,833 patients in the training cohort, 453 (11.8%) had AIHD; of the 1,145 patients in the validation cohort, 166 (14.5%) had AIHD. XGB, troponin (single-variable), and logistic regression models showed similar discrimination power (AUROC [95% confidence interval]: XGB model, 0.75 [0.71–0.79]; troponin model, 0.73 [0.69–0.77]; logistic regression model, 0.73 [0.70–0.79]). Most patients were classified as non-AIHD; calibration was good in patients with a low predicted probability of AIHD in all prediction models. Unlike in the logistic regression model, a nonlinear relationship-like threshold and U-shaped relationship between variables and the probability of AIHD were revealed in the XGB model. Conclusion We developed and validated an AIHD prediction model for patients with atypical chest pain by using an XGB model.

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“…In other works, authors focused on another classification strategy that is gaining ground and is proving its efficiency in many domains, and it is trajectory clustering [21], [22]. Whether it is for face recognition, behavioral analysis, or traffic monitoring, one critical step is the extraction of the features from trajectories with different characteristics, especially when data is imbalanced [23]- [25]. We cite [23], in which authors aim to predict the risk of a driver having an accident considering his driving behavior.…”

Section: Related Workmentioning

confidence: 99%

“…Whether it is for face recognition, behavioral analysis, or traffic monitoring, one critical step is the extraction of the features from trajectories with different characteristics, especially when data is imbalanced [23]- [25]. We cite [23], in which authors aim to predict the risk of a driver having an accident considering his driving behavior. The feature set includes behavior features extracted from trajectories of the vehicle in movement and evaluated through XGBoost, and risk levels obtained by training a clustering algorithm on risk indicator features and also evaluated along with the resulting behavior features through Recursive Feature Elimination (RFE).…”

Section: Related Workmentioning

confidence: 99%

Online Data Center Traffic Classification Based on Inter-Flow Correlations

et al. 2020

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Today, increasing attention is being paid to Data Center (DC) traffic classification since these infrastructures have become the heart of a variety of time-sensitive and data-intensive service platforms. Classification provides the required tools for better understanding traffic patterns in order to ensure high Quality of Service (QoS) performances and solve scalability problems. Unfortunately, existing classification algorithms cannot deal efficiently with two critical challenges in DC traffic: inter-class imbalance and critical time constraints. In this paper, we propose a novel correlation-based algorithm following a cost-sensitive approach combined with a Bagged Random Forest (BRF) ensemble algorithm, to address the inter-class imbalance problem while meeting time requirements in a data center environment. In this strategy, a new method based on Reverse k-Nearest Neighbors (RkNN) is proposed to capture the rebalancing weights expressing inter-flow correlations, in order to perform an online classification approach. We demonstrate the efficiency of the algorithm by comparing its performance to several existing methods from data level, algorithm level, and cost-sensitive strategies on four real-world datasets. The results reveal that the proposed algorithm outperforms most approaches in the different datasets in terms of precision, recall, F1 measure, AUC and Kappa, as opposed to other algorithms that result in either high precision with low recall and low precision and high recall causing congestion or resource over provisioning.

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A feature learning approach based on XGBoost for driving assessment and risk prediction

Cited by 182 publications

References 21 publications

Impact of Systematic Factors on the Outbreak Outcomes of the Novel COVID-19 Disease in China: Factor Analysis Study

Impact of Systematic Factors on the Outbreak Outcomes of the Novel COVID-19 Disease in China: Factor Analysis Study

Emergency department routine data and the diagnosis of acute ischemic heart disease in patients with atypical chest pain

Online Data Center Traffic Classification Based on Inter-Flow Correlations

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