Variable selection and importance in presence of high collinearity: an application to the prediction of lean body mass from multi-frequency bioelectrical impedance

Cammarota, Camillo; Pinto, Alessandro

doi:10.1080/02664763.2020.1763930

Cited by 16 publications

(18 citation statements)

References 34 publications

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“…Our sample size (rows) regarding the features had a proportion of 35 to 1, limiting the model ability to generalize high values of R t even though still being able to approach the trend on average. Similar findings were reported in previous studies where RF showed higher robustness to regression applications compared to SVR and ANNs (Li, Yang et al 2016, Wang, Zhou et al 2016) due to the ability to minimize bias in multidimensional datasets (Cammarota and Pinto 2020). This outcome confirms the influence of collinearity of predictors (one predictor can predict another predictor) for SVR in a multivariate regression problem (Dormann, Elith et al 2013) which led to higher E values for the margin selections.…”

Section: Discussionsupporting

confidence: 90%

Prediction of the effective reproduction number of COVID-19 in Greece. A machine learning approach using Google mobility data

Alexiou

Furxhi

Tasioulis

et al. 2021

Preprint

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Section: Discussionsupporting

confidence: 90%

Prediction of the effective reproduction number of COVID-19 in Greece. A machine learning approach using Google mobility data

Alexiou

Furxhi

Tasioulis

et al. 2021

Preprint

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show abstract

“…Our sample size (rows) regarding the features had a proportion of 35 to 1, limiting the model ability to generalize high values of Rt even though still being able to approach the trend on average. Similar findings were reported in previous studies where RF showed higher robustness to regression applications compared to SVR and ANNs [63,64] due to the ability to minimize bias in multidimensional datasets [65]. This outcome confirms the influence of collinearity of predictors (one predictor can predict another predictor) for SVR in a multivariate regression problem [66] which led to higher values for the margin selections.…”

Section: Managerial Implicationsupporting

confidence: 89%

Prediction of the effective reproduction number of COVID-19 in Greece. A machine learning approach using Google mobility data

Arvanitis

Furxhi

Tasioulis

et al. 2021

J. Decis. Anal. Int. Comp.

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This paper demonstrates how a short-term prediction of the effective reproduction number (Rt) of COVID-19 in regions of Greece is achieved based on online mobility data. Various machine learning methods are applied to predict Rt and attribute importance analysis is performed to reveal the most important variables that affect the accurate prediction of Rt. Work and Park categories are identified as the most important mobility features when compared to the other attributes, with values of 0.25 and 0.24, respectively. Our results are based on an ensemble of diverse Rt methodologies to provide non-precautious and non-indulgent predictions. Random Forest algorithm achieved the highest R2 (0.8 approximately), Pearson’s and Spearman’s correlation values close to 0.9, outperforming in all metrics the other models. The model demonstrates robust results and the methodology overall represents a promising approach towards COVID-19 outbreak prediction. This paper can help health-related authorities when deciding on non-nosocomial interventions to prevent the spread of COVID-19.

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“…Finally, although the contribution of anthropometric measurements in statistical models for the prediction of human body composition (for example, in the estimation of lean body mass) is extremely high and explains over 80% of variability [ 62 ], to explain residual variability, especially in different clinical settings, it is necessary to develop new tools and software that integrate the available analytical methods of human body composition according with the perspective of multicompartmental models.…”

Section: Discussionmentioning

confidence: 99%

Digital Anthropometry: A Systematic Review on Precision, Reliability and Accuracy of Most Popular Existing Technologies

et al. 2023

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Digital anthropometry (DA) has been recently developed for body composition evaluation and for postural analysis. The aims of this review are to examine the current state of DA technology, as well as to verify the methods for identifying the best technology to be used in the field of DA by evaluating the reliability and accuracy of the available technologies on the market, and lay the groundwork for future technological developments. A literature search was performed and 28 studies met the inclusion criteria. The reliability and accuracy of DA was high in most studies, especially in the assessment of patients with obesity, although they varied according to the technology used; a good correlation was found between DA and conventional anthropometry (CA) and body composition estimates. DA is less time-consuming and less expensive and could be used as a screening tool before more expensive imaging techniques or as an alternative to other less affordable techniques. At present, DA could be useful in clinical practice, but the heterogeneity of the available studies (different devices used, laser technologies, population examined, etc.) necessitates caution in the interpretation of the obtained results. Furthermore, the need to develop integrated technologies for analyzing body composition according to multi-compartmental models is increasingly evident.

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Variable selection and importance in presence of high collinearity: an application to the prediction of lean body mass from multi-frequency bioelectrical impedance

Cited by 16 publications

References 34 publications

Prediction of the effective reproduction number of COVID-19 in Greece. A machine learning approach using Google mobility data

Prediction of the effective reproduction number of COVID-19 in Greece. A machine learning approach using Google mobility data

Prediction of the effective reproduction number of COVID-19 in Greece. A machine learning approach using Google mobility data

Digital Anthropometry: A Systematic Review on Precision, Reliability and Accuracy of Most Popular Existing Technologies

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