Assessing Accident Risk using Ordinal Regression and Multinomial Logistic Regression Data Generation

Alicioğlu, Gülsüm; Sun, Bo; Ho, Shen-Shyang

doi:10.1109/ijcnn48605.2020.9207105

Cited by 7 publications

(13 citation statements)

References 16 publications

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“…The ordinal regression models are the extensions of binomial regression, and are also called ranking learning in machine learning. These models are used to predict the dependent variable with ordered multiple categories ( 19 ). Suppose the underlying process to be characterized is…”

Section: Machine Learning Methodsmentioning

confidence: 99%

Prediction of the Fundus Tessellation Severity With Machine Learning Methods

et al. 2022

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PurposeTo predict the fundus tessellation (FT) severity with machine learning methods.MethodsA population-based cross-sectional study with 3,468 individuals (mean age of 64.6 ± 9.8 years) based on Beijing Eye Study 2011. Participants underwent detailed ophthalmic examinations including fundus images. Five machine learning methods including ordinal logistic regression, ordinal probit regression, ordinal log-gamma regression, ordinal forest and neural network were used.Main Outcome MeasureFT precision, recall, F1-score, weighted-average F1-score and AUC value.ResultsObserved from the in-sample fitting performance, the optimal model was ordinal forest, which had correct classification rate (precision) of 81.28%, while 34.75, 93.73, 70.03, and 24.82% in each classified group by FT severity. The AUC value was 0.7249. And the F1-score was 65.05%, weighted-average F1-score was 79.64% on the whole dataset. For out-of-sample prediction performance, the optimal model was ordinal logistic regression, which had precision of 77.12% on the validation dataset, while 19.57, 92.68, 64.74, and 6.76% in each classified group by FT severity. The AUC value was 0.7187. The classification accuracy of light FT group was the highest, while that of severe FT group was the lowest. And the F1-score was 54.46%, weighted-average F1-score was 74.19% on the whole dataset.ConclusionsThe ordinal forest and ordinal logistic regression model had the strong prediction in-sample and out-sample performance, respectively. The threshold ranges of the ordinal forest model for no FT and light, moderate, severe FT were [0, 0.3078], [0.3078, 0.3347], [0.3347, 0.4048], [0.4048, 1], respectively. Likewise, the threshold ranges of ordinal logistic regression model were ≤ 3.7389, [3.7389, 10.5053], [10.5053, 13.9323], > 13.9323. These results can be applied to guide clinical fundus disease screening and FT severity assessment.

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Section: Machine Learning Methodsmentioning

confidence: 99%

Prediction of the Fundus Tessellation Severity With Machine Learning Methods

et al. 2022

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“…However, none of these studies investigate the effect of different NN architectures on the prediction performance for accident and injury severity. Ordinal regression models are used when the classes represent levels of an inherent order [3,11,[25][26][27][28]. Some examples of the applications include evaluating disease severity in plants [26], healthcare applications [11], and assessing credit-rating agencies [25].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Some examples of the applications include evaluating disease severity in plants [26], healthcare applications [11], and assessing credit-rating agencies [25]. This study provides a comprehensive comparison with commonly used ML algorithms in accident risk assessment and ordinal regression models [3].…”

Section: Literature Reviewmentioning

confidence: 99%

“…These intelligent vehicle technologies include functionalities such as determining following distance and perceiving on-road objects [2]. Developing advanced transportation safety systems with the timely prediction of potential traffic accidents and possible injury severity in an intelligent vehicle would ensure road, driver, and passenger safety [2,3]. Designing safe road roundabouts contribute to addressing traffic congestion and increasing pedestrian and road safety [4].…”

Section: Introductionmentioning

confidence: 99%

“…In particular, the warnings by this system allow the intelligent vehicles and drivers to take timely precautions by applying safety maneuvers such as decreasing vehicle speed, automatic braking, automatic lane keeping/control, precise maneuvering, etc. in complex traffic environments [2,3,12].…”

Section: Introductionmentioning

confidence: 99%

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An Injury-Severity-Prediction-Driven Accident Prevention System

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Traffic accidents are inevitable events that occur unexpectedly and unintentionally. Therefore, analyzing traffic data is essential to prevent fatal accidents. Traffic data analysis provided insights into significant factors and driver behavioral patterns causing accidents. Combining these patterns and the prediction model into an accident prevention system can assist in reducing and preventing traffic accidents. This study applied various machine learning models, including neural network, ordinal regression, decision tree, support vector machines, and logistic regression to have a robust prediction model in injury severity. The trained model provides timely and accurate predictions on accident occurrence and injury severity using real-world traffic accident datasets. We proposed an informative negative data generator using feature weights derived from multinomial logit regression to balance the non-fatal accident data. Our aim is to resolve the bias that happens in the favor of the majority class as well as performance improvement. We evaluated the overall and class-level performance of the machine learning models based on accuracy and mean squared error scores. Three hidden layered neural networks outperformed the other models with 0.254 ± 0.038 and 0.173 ± 0.016 MSE scores for two different datasets. A neural network, which provides more accurate and reliable results, should be integrated into the accident prevention system.

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Assessing Accident Risk using Ordinal Regression and Multinomial Logistic Regression Data Generation

Cited by 7 publications

References 16 publications

Prediction of the Fundus Tessellation Severity With Machine Learning Methods

Prediction of the Fundus Tessellation Severity With Machine Learning Methods

An Injury-Severity-Prediction-Driven Accident Prevention System

ViewClassifier: Visual Analytics on Performance Analysis for Imbalanced Fatal Accident Data

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