Application of data mining algorithms for improving stress prediction of automobile drivers: A case study in Jordan

Hadi, Wael; El-Khalili, Nuha; Al-Nashashibi, May; Issa, Ghassan F.; AlBanna, Abed Alkarim

doi:10.1016/j.compbiomed.2019.103474

Cited by 25 publications

(11 citation statements)

References 24 publications

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“…Stress has proven to be an important factor in traffic safety, and a growing number of researchers are attempting to detect the driver's stress status [3], [4]. Although previous research results have been able to achieve a high prediction accuracy, the models rely excessively on physiological data and no physiological data acquisition device exists for the vehicle assistance system, making it difficult to apply the previous research to daily driving tasks [1].…”

Section: Discussionmentioning

confidence: 99%

XGBoost Algorithm-Based Monitoring Model for Urban Driving Stress: Combining Driving Behaviour, Driving Environment, and Route Familiarity

Guo

et al. 2021

IEEE Access

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Stress is considered by many studies to affect traffic safety, and many researchers have attempted to monitor the dynamics of driving stress. Previous research has relied excessively on the positive effects of psychological indicators to improve the accuracy of stress monitoring models. However, psychological data collection sensors have not been widely used in conventional vehicles, which makes it impossible to apply the results of that research to actual driving tasks on a daily basis, even if the accuracy is high. This study designs a real driving task to extract data and proposes a driver's driving stress monitoring model based on driving behaviour, driving environment, and route familiarity. The driving behaviour is described by the speed and acceleration of the vehicle, and the driving environment is quantified by a dilated residual networks (DRN) model thazt divides the video image from the full region into subregions according to the distribution of the driver's attention. Based on the psychological data and driver stress inventory (DSI) results, the study used a K-means 3D cluster analysis to obtain the evaluation method of driving stress and constructed an extreme gradient boosting (XGBoost) model to monitor driving stress. Comparisons of performance with other models show that the XGBoost model significantly outperforms the other three mainstream machine learning algorithms and exceeds most traditional models without the use of psychological data. The model's performance indicators, accuracy, sensitivity, and precision, reached 91.18%-93.25%, 84.13%-89.37%, and 90.25%-91.34%, respectively. The study also summarises the ranking of effects of different scene elements on driving stress for each visual field. The results could make it possible to apply stress monitoring on a large scale to real driving situations, providing urban designers with advice on how to reduce driver stress and directing their attention to those visual areas and visual scene elements that have a higher impact on driving stress and need improvement.

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Section: Discussionmentioning

confidence: 99%

XGBoost Algorithm-Based Monitoring Model for Urban Driving Stress: Combining Driving Behaviour, Driving Environment, and Route Familiarity

Guo

et al. 2021

IEEE Access

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“…Though cyberchondriacs may assume that their internet searches will provide them with answers, it is never enough to satisfy them and their dramatic health concerns. Instead, cyberchondriacs who want to heal from their condition must focus on breaking their continuous and harmful cycle of worrying about a symptom and then checking the internet for hours, even days, on end in order to find the answers that they are searching for [32,33,34,35,36].…”

Section: Proposed Solutionmentioning

confidence: 99%

The Internet’s Role in Undermining the Credibility of the Healthcare Industry

Radwan

2022

Int. J. CIM

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The widespread popularity of the internet can be considered both advantageous and detrimental to society, as the age of technological advancement sparks innovative ways in which people receive information. On one hand, the availability of the internet has allowed people to research their own health concerns and symptoms, which enables them to seek treatment faster. On the other hand, this can turn into an obsessive habit by cyberchondriacs who relentlessly scour the internet over any trivial health concern. Furthermore, the widespread popularity of the internet is also responsible for a surge in spreading misinformation, namely about the COVID-19 pandemic, which has led to undermining the credibility of trusted healthcare officials and organizations. This paper aims to not only explore the problems that arise with the widespread availability of information via the internet, but to also explore possible solutions that would benefit society on both an individualized and collective scale. The solutions presented in this paper seek to assist individuals in rendering professional therapy services for Cognitive Behavior Therapy, and to assist the collective population in maintaining responsible internet usage.

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“…On account of this fact, the machine learning classifiers are more biased towards majority classes, and the machine learning models are much more likely to classify new observations to the majority class. As a result, the imbalanced data problem can cause poor classification for minority classes [33,34].…”

Section: ) Imbalanced Data Problemmentioning

confidence: 99%

A New Hybrid Predictive Model to Predict the Early Mortality Risk in Intensive Care Units on a Highly Imbalanced Dataset

et al. 2020

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Due to the development of biomedical equipment and healthcare level, especially in the Intensive Care Unit (ICU), a considerable amount of data has been collected for analysis. Mortality prediction in the ICUs is considered as one of the most important topics in the healthcare data analysis section. A precise prediction of the mortality risk for patients in ICU could provide us with valuable information about patients' lives and reduce costs at the earliest possible stage. This paper aims to introduce a new hybrid predictive model using the Genetic Algorithm as a feature selection method and a new ensemble classifier based on the combination of Stacking and Boosting ensemble methods to create an early mortality prediction model on a highly imbalanced dataset. The SVM-SMOTE method is used to solve the imbalanced data problem. This paper compares the new model with various machine learning models to validate the efficiency of the introduced model. The achieved results using the shuffle 5-fold cross-validation and random hold-out methods indicate that the new hybrid model has the best performance among other classifiers. Additionally, the Friedman test is applied as a statistical significance test to examine the differences between classifiers. The results of the statistical analysis prove that the proposed model is more effective than other classifiers. Furthermore, the proposed model is compared to APACHE and SAPS scoring systems and is benchmarked against state-of-the-art predictive models applied to the MIMIC dataset for experimental validation and achieved promising results as it outperformed the state-of-the-art models.

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Application of data mining algorithms for improving stress prediction of automobile drivers: A case study in Jordan

Cited by 25 publications

References 24 publications

XGBoost Algorithm-Based Monitoring Model for Urban Driving Stress: Combining Driving Behaviour, Driving Environment, and Route Familiarity

XGBoost Algorithm-Based Monitoring Model for Urban Driving Stress: Combining Driving Behaviour, Driving Environment, and Route Familiarity

The Internet’s Role in Undermining the Credibility of the Healthcare Industry

A New Hybrid Predictive Model to Predict the Early Mortality Risk in Intensive Care Units on a Highly Imbalanced Dataset

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