Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/465184
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dc.date.accessioned2023-02-21T04:46:53Z-
dc.date.available2023-02-21T04:46:53Z-
dc.identifier.urihttp://hdl.handle.net/10603/465184-
dc.description.abstractAbstract newlineOne of the key aspects of Artificial Intelligence (AI) and a persistent prerequisite of newlinehealthcare is to evaluate complicated circumstances, make forecasts, and determine newlinepatterns. Computerized clinical decision support systems (CDSS), reflects a dramatic newlinetransformation in healthcare today using machine learning and computer vision. This newlinefield of AI hopes to incorporate the human capacities of data sensing, data interpretation, newlineand behaviour based on historical and current findings into computers. An newlineevolving area that finds many therapeutic uses is the automated interpretation of newlinefacial expressions and speech analysis. newlineParkinson s disease (PD) is a movement disorder that impacts the neurological system. newlineFacial bradykinesia is a significant motor symptom of PD that results in the newlinereduction and the slowdown of facial movements of patients. Often dysarthria is observed newlinein patients with PD which causes slurred or slow speech that can be difficult newlineto understand. newlineSince there are currently no proven biomarkers for diagnostic tests, conventional PD newlinediagnosis methodologies are primarily on a patient s clinical history and physical newlineexamination. Time-consuming assessments are carried out by qualified medical professionals. newlineIt can become burdensome if periodic re-evaluation is necessary. newlineDevelopment of tools and techniques using computer vision and machine analysis may newlineturn into an alternative automated evaluation in verbal and non-verbal platforms in newlinethis scenario. This leads to the motive of this research work as to develop automated newlineDecision Support System (DSS) in PD management. Such DSS has the potential to newlineimprove healthcare by bridging the gap between optimal practice and actual clinical newlinecare. newlineA major challenge in the implementation of this research work is the unavailability of newlinethe data (facial expressions images and raw speech data) of patients with PD due to newlineethical constraints. Thus, in the initial phase of implementation, the proposed DSS newlinemodules were trained and tested using data from freel
dc.format.extent
dc.languageEnglish
dc.relation
dc.rightsuniversity
dc.titleDecision Support System For Clinical Diagnosis Based On Emotion Detection
dc.title.alternativeDecision Support System For Clinical Diagnosis Based On Emotion Detection
dc.creator.researcherSonawane, Bhakti
dc.subject.keywordfacial expressions
dc.subject.keywordraw speech data
dc.subject.keywordtherapeutic
dc.description.note
dc.contributor.guideSharma, Priyanka
dc.publisher.placeAhmedabad
dc.publisher.universityNirma University
dc.publisher.institutionInstitute of Technology
dc.date.registered2015
dc.date.completed2022
dc.date.awarded2022
dc.format.dimensions
dc.format.accompanyingmaterialDVD
dc.source.universityUniversity
dc.type.degreePh.D.
Appears in Departments:Institute of Technology

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01_tittle.pdfAttached File29.61 kBAdobe PDFView/Open
02_prelim pages.pdf1.23 MBAdobe PDFView/Open
03_content.pdf96.94 kBAdobe PDFView/Open
04_abstract.pdf59.9 kBAdobe PDFView/Open
05_chapter_1.pdf94.86 kBAdobe PDFView/Open
06_chapter_2.pdf5.3 MBAdobe PDFView/Open
07_chapter_3.pdf177.31 kBAdobe PDFView/Open
08_chapter_4.pdf3.63 MBAdobe PDFView/Open
09_chapter_5.pdf479.35 kBAdobe PDFView/Open
10_chapter_6.pdf4.95 MBAdobe PDFView/Open
11_chapter_7.pdf209.99 kBAdobe PDFView/Open
12_chapter_8.pdf588.4 kBAdobe PDFView/Open
13_chapter_09.pdf57.95 kBAdobe PDFView/Open
14_annexures.pdf5.42 MBAdobe PDFView/Open
80_recommendation.pdf113.92 kBAdobe PDFView/Open


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