Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/716997
Title: Machine Learning Based Classification Using Clinical and DaTSCAN SPECT Imaging Features: A Study on Parkinson’s Disease and SWEDD
Other Titles: IEEE Transactions on Radiation and Plasma Medical Sciences
Authors: Rostom Mabrouk|Belkacem Chikhaoui|Layachi Bentabet
subject: random forest (RF)|machine learning (ML)|support vector machine (SVM)|Parkinson’s disease (PD)|logistic regression (LR)|K-nearest neighbor (K-NN)|multilayer perceptron (MLP)|single-photon emission computed tomography (SPECT)|scan without evidence of dopaminergic deficit (SWEDD)
Year: -1-Uns- -1
Abstract: DatSCAN single-photon emission computed tomography (SPECT) imaging is a reliable method to assess dopaminergic transporter in degenerative Parkinsonism. Scan without evidence of dopaminergic deficit (SWEDD) subjects are clinically diagnosed as Parkinson’s disease (PD) patients although the SPECT imaging does not show any negro-striatal abnormality. In this paper, five models of machine learning were used to carry out binary classification [healthy control (HC)/PD] using clinical assessment and image-derived features applied thereafter on SWEDD group as a potential application of motor and nonmotors features in understanding PD characteristic in this group. The nested cross-validation was an essential component to select reliable models. A high accuracy was achieved for the five models (75.4%–78.4% for motor features and 71%–82.2% for nonmotor features) in binary classification (HCs versus PD). Accordingly, we demonstrate the suitability and usefulness of ML models to carry out binary classification of SPECT data. Cross all models applied on SWEDD group, 17.6% of patients were classified as PD motor disorder lookalikes, 27.4% were classified as having a beginning nonmotor abnormality of PD, and 3.9% were classified as having both motor and nonmotor PD features. However, the interpretability of SWEDD predicted condition must be carefully considered.
URI: http://localhost/handle/Hannan/716997
ISBN: 2469-7311
volume: Volume
issue: Issue
Appears in Collections:New Ieee 2019

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Title: Machine Learning Based Classification Using Clinical and DaTSCAN SPECT Imaging Features: A Study on Parkinson’s Disease and SWEDD
Other Titles: IEEE Transactions on Radiation and Plasma Medical Sciences
Authors: Rostom Mabrouk|Belkacem Chikhaoui|Layachi Bentabet
subject: random forest (RF)|machine learning (ML)|support vector machine (SVM)|Parkinson’s disease (PD)|logistic regression (LR)|K-nearest neighbor (K-NN)|multilayer perceptron (MLP)|single-photon emission computed tomography (SPECT)|scan without evidence of dopaminergic deficit (SWEDD)
Year: -1-Uns- -1
Abstract: DatSCAN single-photon emission computed tomography (SPECT) imaging is a reliable method to assess dopaminergic transporter in degenerative Parkinsonism. Scan without evidence of dopaminergic deficit (SWEDD) subjects are clinically diagnosed as Parkinson’s disease (PD) patients although the SPECT imaging does not show any negro-striatal abnormality. In this paper, five models of machine learning were used to carry out binary classification [healthy control (HC)/PD] using clinical assessment and image-derived features applied thereafter on SWEDD group as a potential application of motor and nonmotors features in understanding PD characteristic in this group. The nested cross-validation was an essential component to select reliable models. A high accuracy was achieved for the five models (75.4%–78.4% for motor features and 71%–82.2% for nonmotor features) in binary classification (HCs versus PD). Accordingly, we demonstrate the suitability and usefulness of ML models to carry out binary classification of SPECT data. Cross all models applied on SWEDD group, 17.6% of patients were classified as PD motor disorder lookalikes, 27.4% were classified as having a beginning nonmotor abnormality of PD, and 3.9% were classified as having both motor and nonmotor PD features. However, the interpretability of SWEDD predicted condition must be carefully considered.
URI: http://localhost/handle/Hannan/716997
ISBN: 2469-7311
volume: Volume
issue: Issue
Appears in Collections:New Ieee 2019

Files in This Item:
File Description SizeFormat 
08502866.pdf1.01 MBAdobe PDFThumbnail
Preview File
Title: Machine Learning Based Classification Using Clinical and DaTSCAN SPECT Imaging Features: A Study on Parkinson’s Disease and SWEDD
Other Titles: IEEE Transactions on Radiation and Plasma Medical Sciences
Authors: Rostom Mabrouk|Belkacem Chikhaoui|Layachi Bentabet
subject: random forest (RF)|machine learning (ML)|support vector machine (SVM)|Parkinson’s disease (PD)|logistic regression (LR)|K-nearest neighbor (K-NN)|multilayer perceptron (MLP)|single-photon emission computed tomography (SPECT)|scan without evidence of dopaminergic deficit (SWEDD)
Year: -1-Uns- -1
Abstract: DatSCAN single-photon emission computed tomography (SPECT) imaging is a reliable method to assess dopaminergic transporter in degenerative Parkinsonism. Scan without evidence of dopaminergic deficit (SWEDD) subjects are clinically diagnosed as Parkinson’s disease (PD) patients although the SPECT imaging does not show any negro-striatal abnormality. In this paper, five models of machine learning were used to carry out binary classification [healthy control (HC)/PD] using clinical assessment and image-derived features applied thereafter on SWEDD group as a potential application of motor and nonmotors features in understanding PD characteristic in this group. The nested cross-validation was an essential component to select reliable models. A high accuracy was achieved for the five models (75.4%–78.4% for motor features and 71%–82.2% for nonmotor features) in binary classification (HCs versus PD). Accordingly, we demonstrate the suitability and usefulness of ML models to carry out binary classification of SPECT data. Cross all models applied on SWEDD group, 17.6% of patients were classified as PD motor disorder lookalikes, 27.4% were classified as having a beginning nonmotor abnormality of PD, and 3.9% were classified as having both motor and nonmotor PD features. However, the interpretability of SWEDD predicted condition must be carefully considered.
URI: http://localhost/handle/Hannan/716997
ISBN: 2469-7311
volume: Volume
issue: Issue
Appears in Collections:New Ieee 2019

Files in This Item:
File Description SizeFormat 
08502866.pdf1.01 MBAdobe PDFThumbnail
Preview File