Sign-Consistency Based Variable Importance for Machine Learning in Brain Imaging

AbstractAn important problem that hinders the use of supervised classification algorithms for brain imaging is that the number of variables per single subject far exceeds the number of training subjects available. Deriving multivariate measures of variable importance becomes a challenge in such scenarios. This paper proposes a new measure of variable importance termed sign-consistency bagging (SCB). The SCB captures variable importance by analyzing the sign consistency of the corresponding weights in an ensemble of linear support vector machine (SVM) classifiers. Further, the SCB variable importances are enhanced by means of transductive conformal analysis. This extra step is important when the data can be assumed to be heterogeneous. Finally, the proposal of these SCB variable importance measures is completed with the derivation of a parametric hypothesis test of variable importance. The new importance measures were compared with a t-test based univariate and an SVM-based multivariate variable importances using anatomical and functional magnetic resonance imaging data. The obtained results demonstrated that the new SCB based importance measures were superior to the compared methods in terms of reproducibility and classification accuracy.

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Intranasal vasopressin modulates resting state brain activity across multiple neural systems: evidence from a brain imaging machine learning study

Neuropharmacology ◽

10.1016/j.neuropharm.2021.108561 ◽

2021 ◽

pp. 108561

Author(s):

Xinling Chen ◽

Yongbo Xu ◽

Bingjie Li ◽

Xiaoyan Wu ◽

Ting Li ◽

...

Keyword(s):

Machine Learning ◽

Brain Imaging ◽

Resting State ◽

Brain Activity ◽

Neural Systems ◽

Learning Study

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Temporal and Spatial Autocorrelation as Determinants of Regional AOD-PM2.5 Model Performance in the Middle East

Remote Sensing ◽

10.3390/rs13183790 ◽

2021 ◽

Vol 13 (18) ◽

pp. 3790

Author(s):

Khang Chau ◽

Meredith Franklin ◽

Huikyo Lee ◽

Michael Garay ◽

Olga Kalashnikova

Keyword(s):

Machine Learning ◽

Middle East ◽

United Arab Emirates ◽

Atmospheric Correction ◽

Predictive Performance ◽

Variable Importance ◽

Learning Models ◽

Median Test ◽

Temporal And Spatial ◽

Machine Learning Models

Exposure to fine particulate matter (PM2.5) air pollution has been shown in numerous studies to be associated with detrimental health effects. However, the ability to conduct epidemiological assessments can be limited due to challenges in generating reliable PM2.5 estimates, particularly in parts of the world such as the Middle East where measurements are scarce and extreme meteorological events such as sandstorms are frequent. In order to supplement exposure modeling efforts under such conditions, satellite-retrieved aerosol optical depth (AOD) has proven to be useful due to its global coverage. By using AODs from the Multiangle Implementation of Atmospheric Correction (MAIAC) of the MODerate Resolution Imaging Spectroradiometer (MODIS) and the Multiangle Imaging Spectroradiometer (MISR) combined with meteorological and assimilated aerosol information from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), we constructed machine learning models to predict PM2.5 in the area surrounding the Persian Gulf, including Kuwait, Bahrain, and the United Arab Emirates (U.A.E). Our models showed regional differences in predictive performance, with better results in the U.A.E. (median test R2 = 0.66) than Kuwait (median test R2 = 0.51). Variable importance also differed by region, where satellite-retrieved AOD variables were more important for predicting PM2.5 in Kuwait than in the U.A.E. Divergent trends in the temporal and spatial autocorrelations of PM2.5 and AOD in the two regions offered possible explanations for differences in predictive performance and variable importance. In a test of model transferability, we found that models trained in one region and applied to another did not predict PM2.5 well, even if the transferred model had better performance. Overall the results of our study suggest that models developed over large geographic areas could generate PM2.5 estimates with greater uncertainty than could be obtained by taking a regional modeling approach. Furthermore, development of methods to better incorporate spatial and temporal autocorrelations in machine learning models warrants further examination.

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Variable Importance Analysis in Default Prediction using Machine Learning Techniques

Proceedings of the 7th International Conference on Data Science, Technology and Applications ◽

10.5220/0006872400560062 ◽

2018 ◽

Author(s):

Başak Gültekin ◽

Betül Erdoğdu Şakar

Keyword(s):

Machine Learning ◽

Variable Importance ◽

Machine Learning Techniques ◽

Learning Techniques ◽

Default Prediction ◽

Importance Analysis

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Machine learning approaches to the determinants of women’s vasomotor symptoms using general hospital data

10.21203/rs.3.rs-74144/v1 ◽

2020 ◽

Author(s):

Ki-Jin Ryu ◽

Kyong Wook Yi ◽

Yong Jin Kim ◽

Jung Ho Shin ◽

Jun Young Hur ◽

...

Keyword(s):

Machine Learning ◽

Insulin Resistance ◽

Random Forest ◽

Variable Importance ◽

Vasomotor Symptoms ◽

Model Assessment ◽

Cancer Antigen ◽

Learning Methods ◽

Machine Learning Methods ◽

Glutamyl Transferase

Abstract Background To analyze the determinants of women’s vasomotor symptoms (VMS) using machine learning. Methods Data came from Korea University Anam Hospital in Seoul, Korea, with 3298 women, aged 40–80 years, who attended their general health check from January 2010 to December 2012. Five machine learning methods were applied and compared for the prediction of VMS, measured by a Menopause Rating Scale. Variable importance, the effect of a variable on model performance, was used for identifying major determinants of VMS. Results In terms of the mean squared error, the random forest (0.9326) was much better than linear regression (12.4856) and artificial neural networks with one, two and three hidden layers (1.5576, 1.5184 and 1.5833, respectively). Based on variable importance from the random forest, the most important determinants of VMS were age, menopause age, thyroid stimulating hormone, monocyte and triglyceride, as well as gamma glutamyl transferase, blood urea nitrogen, cancer antigen 19 − 9, C-reactive protein and low-density-lipoprotein cholesterol. Indeed, the following determinants ranked within the top 20 in terms of variable importance: cancer antigen 125, total cholesterol, insulin, free thyroxine, forced vital capacity, alanine aminotransferase, forced expired volume in one second, height, homeostatic model assessment for insulin resistance and carcinoembryonic antigen. Conclusions Machine learning provides an invaluable decision support system for the prediction of VMS. For preventing VMS, preventive measures would be needed regarding the thyroid function, the lipid profile, the liver function, inflammation markers, insulin resistance, the monocyte, cancer antigens and the lung function.

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