Support Vector Regression-based Model to Analyze Prognosis of Infants with Congenital Muscular Torticollis

Seo, Suk Tae; Lee, In Hee; Son, Chang-Sik; Park, Hee Joon; Park, Hyoung Seob; Yoon, Hyuck Jun; Kim, Yoon Nyun

doi:10.4258/hir.2010.16.4.224

Cited by 8 publications

(5 citation statements)

References 11 publications

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“…SVM is a popular machine learning algorithm that was first introduced to address binary classification tasks (38) and then extended to regression tasks. The regression version has yielded successful applications in numerous fields, including time series prediction (39), energy forecasting (40,41), recognition (42), and medicine (43). We used the implementation provided in the Python package scikit-learn (44).…”

Section: Model 2: Support Vector Regressionmentioning

confidence: 99%

Ensemble Learning of Convolutional Neural Network, Support Vector Machine, and Best Linear Unbiased Predictor for Brain Age Prediction: ARAMIS Contribution to the Predictive Analytics Competition 2019 Challenge

et al. 2020

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We ranked third in the Predictive Analytics Competition (PAC) 2019 challenge by achieving a mean absolute error (MAE) of 3.33 years in predicting age from T1-weighted MRI brain images. Our approach combined seven algorithms that allow generating predictions when the number of features exceeds the number of observations, in particular, two versions of best linear unbiased predictor (BLUP), support vector machine (SVM), two shallow convolutional neural networks (CNNs), and the famous ResNet and Inception V1. Ensemble learning was derived from estimating weights via linear regression in a hold-out subset of the training sample. We further evaluated and identified factors that could influence prediction accuracy: choice of algorithm, ensemble learning, and features used as input/MRI image processing. Our prediction error was correlated with age, and absolute error was greater for older participants, suggesting to increase the training sample for this subgroup. Our results may be used to guide researchers to build age predictors on healthy individuals, which can be used in research and in the clinics as non-specific predictors of disease status.

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Section: Model 2: Support Vector Regressionmentioning

confidence: 99%

Ensemble Learning of Convolutional Neural Network, Support Vector Machine, and Best Linear Unbiased Predictor for Brain Age Prediction: ARAMIS Contribution to the Predictive Analytics Competition 2019 Challenge

et al. 2020

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“…For the SVR analysis, both training and test data were used. 52 We took discriminators DF3 and DF4 for both the ACC data and the EMG data as the independent variable, owing to their performance, while clinicals scores were taken as the dependent variable. We observed a significant correlation ( r > 0.5) between the discriminators, when combinations of all discriminators were taken as independent variables, and clinical scores for all tremor types [PD: UPDRS - DF3 acc : 0.72, UPDRS - DF4 acc : 0.73, UPDRS - DF3 EMG : 0.71 UPDRS - DF4 EMG : 0.74; ET: FTM - DF3 acc : 0.72, FTM - DF4 acc : 0.71, FTM - DF3 EMG : 0.73, FTM - DF4 EMG : 0.73; MS: FTM - DF3 acc : 0.75, FTM - DF4 acc : 0.76, FTM - DF3 EMG : 0.77, FTM - DF4 EMG : 0.75].…”

Section: Resultsmentioning

confidence: 99%

Machine learning aided classification of tremor in multiple sclerosis

et al. 2022

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“…SVMs have been successfully applied to many classification and function prediction tasks, in various fields including science, engineering, and social sciences . SVM‐based classification methods have also been used for surgical applications, while similar regression techniques have been used to a comparatively lesser extent in medical applications . Our analysis was performed using LibSVM (National Taiwan University, http://www.csie.ntu.edu.tw/∼cjlin/libsvm/).…”

Section: Methodsmentioning

confidence: 99%

Bone mineral density and donor age are not predictive of femoral ring allograft bone mechanical strength

2014

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While metal or plastic interbody spinal fusion devices are manufactured to appropriate mechanical standards, mechanical properties of commercially prepared structural allograft bone remain relatively unassessed. Robust models predicting compressive load to failure of structural allograft bone based on easily measured variables would be useful. Three hundred twenty seven femoral rings from 34 cadaver femora were tested to failure in axial compression. Predictive variables included age, gender, bone mineral density (BMD), position along femoral shaft, maximum/minimum wall thickness, outer/inner diameter, and area. We used support vector regression and 10-fold cross-validation to develop robust nonlinear predictive models for load to failure. Model performance was measured by the root-mean-squared-deviation (RMSD) and correlation coefficients (r). A polynomial model using all variables had RMSD ¼ 7.92, r ¼ 0.84, indicating excellent performance. A model using all variables except BMD was essentially unchanged (RMSD ¼ 8.12, r ¼ 0.83). Eliminating both age and BMD produced a model with RMSD ¼ 8.41, r ¼ 0.82, again essentially unchanged. Compressive strength of structural allograft bone can be estimated using easily measured geometric parameters, without including BMD or age. As DEXA is costly and cumbersome, and setting upper age-limits for potential donors reduces the supply, our results may prove helpful to increase the quality and availability of structural allograft. Keywords: structural bone allograft; spinal fusion; biomechanics; bone mineral densityThe allograft bone industry is guided by practices intended to optimize safety and effectiveness of allograft bone in numerous clinical applications. Guidelines have been developed and used with respect to avoidance of transmission of neoplastic and infectious disease based on high level scientific data. 1,2 Adherence to these guidelines has resulted in safe and successful allograft implantation over many years of experience. 3 Despite this excellent track record, the mechanical properties of what is essentially a mechanical device 4 remain comparatively un-assessed. In fact, there are no guidelines prescribed with respect to material properties of structural allograft bone products within the Food and Drug Administration or the American Association of Tissue Banks. 2,5 A recent survey of allograft providers demonstrated that given this lack of guidelines, a variety of practices are employed with regard to screening of donors and allografts. 6 On the other hand, interbody spacers manufactured using materials such as carbon fiber, titanium, or polyetheretherketone are expected to have consistent biomechanical properties. 7-9 While the cited advantages of allograft bone over such implants include lower cost and equal or better clinical performance, 10-21 the mechanical performance of structural allograft bone intended for use as an interbody device should be similarly uniform and reliable.We have accumulated a database of load to failure measurements for fem...

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Support Vector Regression-based Model to Analyze Prognosis of Infants with Congenital Muscular Torticollis

Cited by 8 publications

References 11 publications

Ensemble Learning of Convolutional Neural Network, Support Vector Machine, and Best Linear Unbiased Predictor for Brain Age Prediction: ARAMIS Contribution to the Predictive Analytics Competition 2019 Challenge

Ensemble Learning of Convolutional Neural Network, Support Vector Machine, and Best Linear Unbiased Predictor for Brain Age Prediction: ARAMIS Contribution to the Predictive Analytics Competition 2019 Challenge

Machine learning aided classification of tremor in multiple sclerosis

Bone mineral density and donor age are not predictive of femoral ring allograft bone mechanical strength

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