A Comparative Study on Remote Tracking of Parkinson’s Disease Progression Using Data Mining Methods

Mohammadi, Peyman; Hatamlou, Abdolreza; Màsdàrí, Mohàmmàd

doi:10.5121/ijfcst.2013.3607

Cited by 4 publications

(2 citation statements)

References 31 publications

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“…As an extension of the SMO algorithm proposed by (Platt, 1999) for SVM classifier design, SMOreg overcomes the issue of an important source of confusion and inefficiency caused by SMO. It globally normalizes all attributes by default (Mohammadi et al, 2013), with an excellent performance on handling big samples.…”

Section: Methodsmentioning

confidence: 99%

Predicting individualized clinical measures by a generalized prediction framework and multimodal fusion of MRI data

et al. 2017

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Neuroimaging techniques have greatly enhanced the understanding of neurodiversity (human brain variation across individuals) in both health and disease. The ultimate goal of using brain imaging biomarkers is to perform individualized predictions. Here we proposed a generalized framework that can predict explicit values of the targeted measures by taking advantage of joint information from multiple modalities. This framework also enables whole brain voxel-wise searching by combining multivariate techniques such as ReliefF, clustering, correlation-based feature selection and multiple regression models, which is more flexible and can achieve better prediction performance than alternative atlas-based methods. For 50 healthy controls and 47 schizophrenia patients, three kinds of features derived from resting-state fMRI (fALFF), sMRI (gray matter) and DTI (fractional anisotropy) were extracted and fed into a regression model, achieving high prediction for both cognitive scores (MCCB composite r = 0.7033, MCCB social cognition r = 0.7084) and symptomatic scores (positive and negative syndrome scale [PANSS] positive r = 0.7785, PANSS negative r = 0.7804). Moreover, the brain areas likely responsible for cognitive deficits of schizophrenia, including middle temporal gyrus, dorsolateral prefrontal cortex, striatum, cuneus and cerebellum, were located with different weights, as well as regions predicting PANSS symptoms, including thalamus, striatum and inferior parietal lobule, pinpointing the potential neuromarkers. Finally, compared to a single modality, multimodal combination achieves higher prediction accuracy and enables individualized prediction on multiple clinical measures. There is more work to be done, but the current results highlight the potential utility of multimodal brain imaging biomarkers to eventually inform clinical decision-making.

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Section: Methodsmentioning

confidence: 99%

Predicting individualized clinical measures by a generalized prediction framework and multimodal fusion of MRI data

et al. 2017

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“…The authors of the paper [3] have been used various data mining methods for the prediction of Parkinson diseases. The authors of the paper [4] also used various data mining methods with the data set consisting various vocal attribute of Parkinson disease affected persons. The authors of the paper [5] are developed by the voice measurements of disease mainly focuses the speech signals.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Based Approaches for Prediction of Parkinson's Disease

Tiwari¹

2016

MLAIJ

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Addressing voice recording replications for tracking Parkinson’s disease progression

Naranjo

Pérez

2016

Med Biol Eng Comput

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Tracking Parkinson's disease symptom severity by using characteristics automatically extracted from voice recordings is a very interesting and challenging problem. In this context, voice features are automatically extracted from multiple voice recordings from the same subjects. In principle, for each subject, the features should be identical at a concrete time, but the imperfections in technology and the own biological variability result in nonidentical replicated features. The involved within-subject variability must be addressed since replicated measurements from voice recordings can not be directly used in independence-based pattern recognition methods as they have been routinely used through the scientific literature. Besides, the time plays a key role in the experimental design. In this paper, for the first time, a Bayesian linear regression approach suitable to handle replicated measurements and time is proposed. Moreover, a version favoring the best predictors and penalizing the worst ones is also presented. Computational difficulties have been avoided by developing Gibbs sampling-based approaches.

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A Comparative Study on Remote Tracking of Parkinson’s Disease Progression Using Data Mining Methods

Cited by 4 publications

References 31 publications

Predicting individualized clinical measures by a generalized prediction framework and multimodal fusion of MRI data

Predicting individualized clinical measures by a generalized prediction framework and multimodal fusion of MRI data

Machine Learning Based Approaches for Prediction of Parkinson's Disease

Addressing voice recording replications for tracking Parkinson’s disease progression

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