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dc.contributor.authorRostom Mabrouk|Belkacem Chikhaoui|Layachi Bentabeten_US
dc.date.accessioned2013en_US
dc.date.accessioned2021-05-16T17:43:34Z-
dc.date.available2021-05-16T17:43:34Z-
dc.date.issueden_US
dc.identifier.isbn2469-7311en_US
dc.identifier.other10.1109/TRPMS.2018.2877754en_US
dc.identifier.urihttp://localhost/handle/Hannan/716997-
dc.description.abstractDatSCAN 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.en_US
dc.relation.haspart08502866.pdfen_US
dc.subjectrandom 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)en_US
dc.titleMachine Learning Based Classification Using Clinical and DaTSCAN SPECT Imaging Features: A Study on Parkinson’s Disease and SWEDDen_US
dc.title.alternativeIEEE Transactions on Radiation and Plasma Medical Sciencesen_US
dc.typeArticleen_US
dc.journal.volumeVolumeen_US
dc.journal.issueIssueen_US
dc.journal.titleIEEE Transactions on Radiation and Plasma Medical Sciencesen_US
Appears in Collections:New Ieee 2019

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dc.contributor.authorRostom Mabrouk|Belkacem Chikhaoui|Layachi Bentabeten_US
dc.date.accessioned2013en_US
dc.date.accessioned2021-05-16T17:43:34Z-
dc.date.available2021-05-16T17:43:34Z-
dc.date.issueden_US
dc.identifier.isbn2469-7311en_US
dc.identifier.other10.1109/TRPMS.2018.2877754en_US
dc.identifier.urihttp://localhost/handle/Hannan/716997-
dc.description.abstractDatSCAN 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.en_US
dc.relation.haspart08502866.pdfen_US
dc.subjectrandom 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)en_US
dc.titleMachine Learning Based Classification Using Clinical and DaTSCAN SPECT Imaging Features: A Study on Parkinson’s Disease and SWEDDen_US
dc.title.alternativeIEEE Transactions on Radiation and Plasma Medical Sciencesen_US
dc.typeArticleen_US
dc.journal.volumeVolumeen_US
dc.journal.issueIssueen_US
dc.journal.titleIEEE Transactions on Radiation and Plasma Medical Sciencesen_US
Appears in Collections:New Ieee 2019

Files in This Item:
File Description SizeFormat 
08502866.pdf1.01 MBAdobe PDFThumbnail
Preview File
Full metadata record
DC FieldValueLanguage
dc.contributor.authorRostom Mabrouk|Belkacem Chikhaoui|Layachi Bentabeten_US
dc.date.accessioned2013en_US
dc.date.accessioned2021-05-16T17:43:34Z-
dc.date.available2021-05-16T17:43:34Z-
dc.date.issueden_US
dc.identifier.isbn2469-7311en_US
dc.identifier.other10.1109/TRPMS.2018.2877754en_US
dc.identifier.urihttp://localhost/handle/Hannan/716997-
dc.description.abstractDatSCAN 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.en_US
dc.relation.haspart08502866.pdfen_US
dc.subjectrandom 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)en_US
dc.titleMachine Learning Based Classification Using Clinical and DaTSCAN SPECT Imaging Features: A Study on Parkinson’s Disease and SWEDDen_US
dc.title.alternativeIEEE Transactions on Radiation and Plasma Medical Sciencesen_US
dc.typeArticleen_US
dc.journal.volumeVolumeen_US
dc.journal.issueIssueen_US
dc.journal.titleIEEE Transactions on Radiation and Plasma Medical Sciencesen_US
Appears in Collections:New Ieee 2019

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