Proximal support vector machine techniques on medical prediction outcome

Drosou, K.; Koukouvinos, Christos

doi:10.1080/02664763.2016.1177499

Cited by 5 publications

(5 citation statements)

References 8 publications

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“…however, these parameters are not included in the currently proposed model. Furthermore, the modelling approach used in this study was the MLr method, whilst more robust algorithms, such as support vector machine [63], random forest [64], and artificial neural network [65], can also provide accurate prediction models. in the future, these algorithms will be used to increase model predictability.…”

Section: Relative Importance Analysis Of Observed Input Variablesmentioning

confidence: 99%

Archives of Mining Sciences

2022

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The historical datasets at operating mine sites are usually large. Directly applying large datasets to build prediction models may lead to inaccurate results. To overcome the real-world challenges, this study aimed to handle these large datasets using gaussian mixture modelling (gMM) for developing a novel and accurate prediction model of truck productivity. A large dataset of truck haulage collected at operating mine sites was clustered by gMM into three latent classes before the prediction model was built. The labels of these latent classes generated a latent variable. Two multiple linear regression (MLr) models were then constructed, including the ordinary-MLr (o-MLr) and the hybrid gMM-MLr models. The gMM-MLr model incorporated the observed input variables and a latent variable in the form of interaction terms. The o-MLr model was the baseline model and did not involve the latent variable. The gMM-MLr model performed considerably better than the o-MLr model in predicting truck productivity. The interaction terms quantitatively measured the differences in how the observed input variables affected truck productivity in three classes (high, medium, and low truck productivity). The haul distance was the most crucial input variable in the gMM-MLr model. This study provides new insights into handling massive amounts of data in truck haulage datasets and a more accurate prediction model for truck productivity.

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Section: Relative Importance Analysis Of Observed Input Variablesmentioning

confidence: 99%

Archives of Mining Sciences

2022

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“…However, the dichotomous dependent variable has the disadvantage that potential cost savings can not directly be assigned [9]. In addition to regression models, classification models such as Support Vector Machine (SVM) and Decision Tree (DT) methods can be applied [16,17,18]. Classification is the assignment of data objects to a suitable class, whereby, for example, the minimization of the classification error or the maximization of the degree of affiliation are used as performance evaluation criteria [19].…”

Section: Literature Review and Backgroundmentioning

confidence: 99%

“…However, comparing different classification and predictive models, Moturu, Johnson, and Liu [17] show that SVM have the lowest performance. In their study, Bertsimas et al [18] utilize DT to classify high-cost patients. The advantage of decision trees lies in the ability to be easily interpreted, where the importance of an attribute is reflected by its proximity to the root node.…”

Section: Literature Review and Backgroundmentioning

confidence: 99%

Real-time Prediction of the Risk of Hospital Readmissions

Wickramasinghe¹,

Degano²,

McConchie³

2019

Humanizing Technology for a Sustainable Society

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This study aims to identify predictors for patients likely to be readmitted to a hospital within 28 days of discharge and to develop and validate a prediction model for identifying patients at a high risk of readmission. Numerous attempts have been made to build similar predictive models. However, the majority of existing models suffer from at least one of the following shortcomings: the model is not based on Australian Health Data; the model uses insurance claim data, which would not be available in a real-time clinical setting; the model does not consider socio-demographic determinants of health, which have been demonstrated to be predictive of readmission risk; or the model is limited to a particular medical condition and is thus limited in scope. To address these shortcomings, we built several models to predict all-cause 28-day readmission risk and included Socioeconomic Indexes for Areas (SEIFA) data as proxies for socio-demographic determinants of health. Additionally, instead of using insurance claims data, which could require several weeks to process, we built our models using data that is readily available during the inpatient stay or at the time of discharge. The set of default prediction models that were examined include logistic regression, elastic net, random forest and adaptive boosting (Ada Boost). This study examined A not for profit tertiary healthcare organisation from fiscal year 2012-2013 through fiscal year 2017-2018. The out-of-sample results show that all of the models performed similarly and adequately to predict readmission risk.

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“…In addition to regression models, classification models such as Support Vector Machine (SVM) and Decision Tree (DT) methods can be applied [22,23,24]. Classification is the assignment of data objects to a suitable class, whereby, for example, the minimization of the classification error or the maximization of the degree of affiliation are used as performance evaluation criteria [25].…”

Section: Methodsmentioning

confidence: 99%

“…In case of more than two attributes, the separating boundary corresponds to a hyperplane [25]. Drosou and Koukouvinos [22] use SVM to find an optimal hyperplane that separates cost-intensive from "regular" patients. However, comparing different classification and predictive models, Moturu, Johnson, and Liu [23] show that SVM have the lowest performance.…”

Section: Methodsmentioning

confidence: 99%

Predicting high-cost patients by Machine Learning: A case study in an Australian private hospital group

Eigner

Bodendorf

Wickramasinghe

EPiC Series in Computing

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Healthcare is considered a data-intensive industry, offering large data volumes that can, for example, be used as the basis for data-driven decisions in hospital resource planning. A significant aspect in that context is the prediction of cost-intensive patients. The presented paper introduces prediction models to identify patients at risk of causing extensive costs to the hospital. Based on a data set from a private Australian hospital group, four logistic regression models designed and evaluated to predict cost-intensive patients. Each model utilizes different feature sets including attributes gradually available throughout a patient episode. The results show that in particular variables reflecting hospital resources have a high influence on the probability to become a cost-intensive patient. The corresponding prediction model that incorporates attributes describing resource utilization achieves a sensitivity of 94.32% and thus enables an effective prediction of cost-intensive patients.

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Proximal support vector machine techniques on medical prediction outcome

Cited by 5 publications

References 8 publications

Archives of Mining Sciences

Archives of Mining Sciences

Real-time Prediction of the Risk of Hospital Readmissions

Predicting high-cost patients by Machine Learning: A case study in an Australian private hospital group

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