Dataset Information

Selecting Clinically Relevant Gait Characteristics for Classification of Early Parkinson's Disease: A Comprehensive Machine Learning Approach.

ABSTRACT: Parkinson's disease (PD) is the second most common neurodegenerative disease; gait impairments are typical and are associated with increased fall risk and poor quality of life. Gait is potentially a useful biomarker to help discriminate PD at an early stage, however the optimal characteristics and combination are unclear. In this study, we used machine learning (ML) techniques to determine the optimal combination of gait characteristics to discriminate people with PD and healthy controls (HC). 303 participants (119 PD, 184 HC) walked continuously around a circuit for 2-minutes at a self-paced walk. Gait was quantified using an instrumented mat (GAITRite) from which 16 gait characteristics were derived and assessed. Gait characteristics were selected using different ML approaches to determine the optimal method (random forest with information gain and recursive features elimination (RFE) technique with support vector machine (SVM) and logistic regression). Five clinical gait characteristics were identified with RFE-SVM (mean step velocity, mean step length, step length variability, mean step width, and step width variability) that accurately classified PD. Model accuracy for classification of early PD ranged between 73-97% with 63-100% sensitivity and 79-94% specificity. In conclusion, we identified a subset of gait characteristics for accurate early classification of PD. These findings pave the way for a better understanding of the utility of ML techniques to support informed clinical decision-making.

SUBMITTER: Rehman RZU

PROVIDER: S-EPMC6872822 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Selecting Clinically Relevant Gait Characteristics for Classification of Early Parkinson's Disease: A Comprehensive Machine Learning Approach.

Rehman Rana Zia Ur RZU Del Din Silvia S Guan Yu Y Yarnall Alison J AJ Shi Jian Qing JQ Rochester Lynn L

Scientific reports 20191121 1

Parkinson's disease (PD) is the second most common neurodegenerative disease; gait impairments are typical and are associated with increased fall risk and poor quality of life. Gait is potentially a useful biomarker to help discriminate PD at an early stage, however the optimal characteristics and combination are unclear. In this study, we used machine learning (ML) techniques to determine the optimal combination of gait characteristics to discriminate people with PD and healthy controls (HC). 3 ...[more]

PMID: 31754175

Similar Datasets

Project description:Background and aimsMicroRNAs (miRNAs) have been shown to play important roles in many cancers, including breast cancer. The majority of previous studies employed network analysis to identify key miRNAs in cancer progression. However, most of dysregulated miRNA networks were constructed based on the expression variation of miRNAs and target genes.MethodsThe relations between miRNAs and target genes were computed by Spearman correlation separately in breast cancer and normal breast samples. We calculated dysregulated scores based on the dysregulation of miRNA-mRNA regulatory relations. A dysregulated miRNA target network (DMTN) was constructed from the miRNA-mRNA pairs with significant dysregulated scores. SVM classifier was employed to predict breast cancer risk miRNAs from the DMTN. Hypermetric test was utilized to calculate the significance of overlap between different gene sets. Pearson correlation was used to evaluate associations between miRNAs/genes and drug response.ResultsThe DMTN comprised 511 miRNAs and was similar to common biological networks. Based on miRNAs and target genes in DMTN, we predicted 90 breast cancer risk miRNAs by using SVM classifier. Predicted risk miRNAs and one-step neighbor genes were significantly overlapping with differential miRNAs, cancer-related and housekeeping genes in breast cancer. These risk miRNAs were involved in many cancer-related and immune-related processes. In addition, most risk miRNAs were able to predict survival of breast cancer patients. More interestingly, some risk miRNAs and one-step neighbor genes were remarkably associated with immune cell infiltration. For example, high expression of hsa-miR-155 indicates high abundance of activated CD4+ T cells but low level of M2 macrophage infiltration. Furthermore, we identified 588 miRNA-drug and 3,146 gene-drug pairs, wherein the expression level of miRNAs/genes could indicate the sensitivity of cancer cells to anti-cancer drugs.ConclusionWe predicted 90 breast cancer risk miRNAs based on proposed DMTN by using SVM classifier. Predicted risk miRNAs are biologically and clinically relevant in breast cancer. Risk miRNAs and one-step neighbor genes could serve as biomarkers for immune cell infiltration and anti-cancer drug response, which sheds lights on immunotherapy or targeted therapy for patients with breast cancer.

Project description:As digital health technology becomes more pervasive, machine learning (ML) provides a robust way to analyze and interpret the myriad of collected features. The purpose of this preliminary work was to use ML classification to assess the benefits and relevance of neurocognitive features both tablet-based assessments and self-reported metrics, as they relate to Parkinson's Disease (PD) and its stages [Hoehn and Yahr (H&Y) Stages 1-5]. Further, this work aims to compare perceived versus sensor-based neurocognitive abilities. In this study, 75 participants ([Formula: see text] PD; [Formula: see text] control) completed 14 tablet-based neurocognitive functional tests (e.g., motor, memory, speech, executive, and multifunction), functional movement assessments (e.g., Berg Balance Scale), and standardized health questionnaires (e.g., PDQ-39). Decision tree classification of sensor-based features allowed for the discrimination of PD from healthy controls with an accuracy of [Formula: see text], and early and advanced stages of PD with an accuracy of [Formula: see text]; compared to the current gold standard tools [e.g., standardized health questionnaires ([Formula: see text] accuracy) and functional movement assessments ([Formula: see text] accuracy)]. Significant features were also identified using decision tree classification. Device magnitude of acceleration was significant in 12 of 14 tests ([Formula: see text]), regardless of test type. For classification between diagnosed and control populations, 17 motor (e.g., device magnitude of acceleration), 9 accuracy (e.g., number of correct/incorrect interactions), and 8 timing features (e.g., time to between interactions) were significant. For classification between early (H&Y Stages 1 and 2) and advanced (H&Y Stages 3, 4, and 5) stages of PD, 7 motor, 12 accuracy, and 14 timing features were significant. Finally, this work depicts that perceived functionality of individuals with PD differed from sensor-based functionalities. In early-stage PD was shown to be [Formula: see text] lower than sensor-based scores with notable perceived deficits in memory and executive function. However, individuals in advanced stages had elevated perceptions (1.57x) for executive and behavioral functions compared to early-stage populations. Machine learning in digital health systems allows for a more comprehensive understanding of neurodegenerative diseases and their stages and may also depict new features that influence the ways digital health technology should be configured.

Project description:Background:To evaluate the association of multiparametric and multiregional MRI-features with key molecular characteristics in patients with newly-diagnosed glioblastoma. Methods:Retrospective data evaluation was approved by the local ethics committee of the University of Heidelberg (ethics approval number: S-320/2012) and informed consent was waived. Preoperative MRI-features were correlated with key molecular characteristics within a single-institutional cohort of 152 patients with newly-diagnosed glioblastoma. Preoperative MRI-features (n=31) included multiparametric (anatomical, diffusion-, perfusion-, and susceptibility-weighted images) and multiregional (contrast enhancing and non-enhancing FLAIR-hyperintense) information with (histogram) quantification of tumor volumes, volume ratios, apparent diffusion coefficients, cerebral blood flow / volume (CBF / CBV) and intratumoral susceptibility signals. Molecular characteristics determined with the Illumina Infinium HumanMethylation450 array included global DNA-methylation subgroups (e.g. mesenchymal (MES), RTK I “PGFRA”, RTK II “classic”), MGMT-promoter methylation status and hallmark copy-number-variations (EGFR-, PDGFRA-, MDM4- and CDK4-amplification; PTEN-, CDKN2A-, NF1- and RB1-loss). Univariate analyses (voxel-lesion-symptom-mapping for tumor location, Wilcoxon-test for all other MRI-features) as well as machine-learning models were applied to study the strength of association and discriminative value of MRI-features for predicting underlying molecular characteristics. Results: There was no tumor location predilection for any of the assessed molecular parameters (permutation-adjusted p>0.05 each). Univariate imaging parameter associations were noted for EGFR amplification and CDKN2A loss, both demonstrating increased nrCBV and nrCBF values (performance of these parameters, as assessed by the area under the ROC curve ranged from 63 to 69%, FDR-adjusted p<0.05, respectively). Subjecting all MRI-features to machine-learning-based classification allowed to predict EGFR amplification status and the RTK II “classic” GB subgroup with a moderate, yet significantly greater accuracy (63% for EGFR [p<0.01] and 61% for RTK II [p=0.01]) than the prediction by chance, whereas prediction accuracy for all other molecular parameters was non-significant (p>0.05, all models). Conclusions: In summary, we found univariate associations between established MRI-features and molecular characteristics, however not of sufficient strength to allow the generation of machine-learning classification models for reliable and clinically meaningful prediction of the assessed molecular characteristics in patients with newly-diagnosed glioblastoma.

Dataset Information

Selecting Clinically Relevant Gait Characteristics for Classification of Early Parkinson's Disease: A Comprehensive Machine Learning Approach.

Publications

Selecting Clinically Relevant Gait Characteristics for Classification of Early Parkinson's Disease: A Comprehensive Machine Learning Approach.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets