2010
DOI: 10.1016/j.bspc.2010.05.004
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Detecting fraudulent whiplash claims by support vector machines

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Cited by 9 publications
(13 citation statements)
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“…Out of a total of 1097 subjects, 52% of them reported dizziness. Cervical vertigo is a matter of considerable concern due to the high litigation‐related costs of whiplash injuries . Dizziness is also reported by subjects with neck injuries due to other processes than whiplash (Table ), although fewer studies are available.…”
Section: Introductionmentioning
confidence: 99%
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“…Out of a total of 1097 subjects, 52% of them reported dizziness. Cervical vertigo is a matter of considerable concern due to the high litigation‐related costs of whiplash injuries . Dizziness is also reported by subjects with neck injuries due to other processes than whiplash (Table ), although fewer studies are available.…”
Section: Introductionmentioning
confidence: 99%
“…Cervical vertigo is a matter of considerable concern due to the high litigation-related costs of whiplash injuries. 4,5 Dizziness is also reported by subjects with neck injuries due to other processes than whiplash (Table I), although fewer studies are available. Animal studies have established that experimental lesions confined to the cervical area can induce nystagmus and ataxia.…”
Section: Introductionmentioning
confidence: 99%
“…Binary fraud/non-fraud classifiers are the most common, although classification can also be applied to detect more specific types of fraud, e.g. fraudulent whiplash (Gudmundsson et al 2010). In the fraud management research related literature one can find different classification algorithms, ranging from simple ones, such as Naïve Bayes, k-Nearest Neighbors and Decision Trees, to the more complex ones, such as Neural Networks and Support Vector Machines (Gudmundsson et al 2010;Viaene et al 2002Viaene et al , 2005Brockett et al 2002).…”
Section: Related Workmentioning
confidence: 99%
“…fraudulent whiplash (Gudmundsson et al 2010). In the fraud management research related literature one can find different classification algorithms, ranging from simple ones, such as Naïve Bayes, k-Nearest Neighbors and Decision Trees, to the more complex ones, such as Neural Networks and Support Vector Machines (Gudmundsson et al 2010;Viaene et al 2002Viaene et al , 2005Brockett et al 2002). A comprehensive analysis and comparison of classification algorithms for fraud detection has been conducted by Viaene (2002).…”
Section: Related Workmentioning
confidence: 99%
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