Parkinson’s disease onset detection Using Machine Learning.

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creativework.keywordsParkinson’s disease (PD),Eye,machine learning
dc.contributor.advisorMd. Shahriar Hussain
dc.contributor.authorMD Samiul Hasan
dc.contributor.authorKazal Roy Sagar
dc.contributor.authorAnika Ferdausy
dc.contributor.id1512466042
dc.contributor.id1430784642
dc.contributor.id1512232042
dc.coverage.departmentElectrical and Computer Engineering
dc.date.accessioned2024-05-14
dc.date.accessioned2024-05-14T06:44:19Z
dc.date.available2024-05-14T06:44:19Z
dc.date.issued2022
dc.description.abstractAccurately identifying Parkinson’s disease (PD) at an early stage is unquestionably critical for halting its progression and giving patients access to disease-modifying medication. To achieve that goal, the premotor stage of Parkinson’s disease should be closely studied. Based on premotor traits, a unique deep-learning technique is introduced to determine if an individual has Parkinson’s disease or not. In order to detect Parkinson’s disease at an early stage, numerous indicators were used in this study, including Rapid Eye Movement and olfactory loss, cerebrospinal fluid data, and dopaminergic imaging markers. A comparison of the proposed deep learning model with twelve machine learning and ensemble learning methods based on relatively small data sets of 183 healthy individuals and 401 early Parkinson’s disease patients reveals that the designed model achieves the highest accuracy, 96.45 percent on average. We present the feature importance of the PD detection procedure based on the Boosting approach in addition to identifying the PD. Traditional diagnostic procedures, on the other hand, may suffer from subjectivity because they rely on the interpretation of motions that are sometimes subtle to human sight and hence difficult to define, potentially leading to misdiagnosis. Meanwhile, early non-motor symptoms of Parkinson’s disease may be moderate and caused by a variety of other illnesses. As a result, these symptoms are frequently missed, making early PD diagnosis difficult. To overcome these challenges and improve PD diagnosis and assessment procedures, machine learning algorithms for the viii classification of PD and healthy controls or patients with similar clinical presentations have been used. A total of 209 papers were included, with pertinent material retrieved and provided in this review, with an examination of their aims, data sources, data kinds, machine learning methods, and associated outcomes. These results show that machine learning methodologies and novel biomarkers have a strong potential for adaption in clinical decision-making, leading to more systematic, informed PD diagnosis.
dc.description.degreeUndergraduate
dc.identifier.cd600000542
dc.identifier.print-thesisTo be assigned
dc.identifier.urihttps://repository.northsouth.edu/handle/123456789/699
dc.language.isoen
dc.publisherNorth South University
dc.rights© NSU Library
dc.titleParkinson’s disease onset detection Using Machine Learning.
dc.typeProject
oaire.citation.endPage40
oaire.citation.startPagei
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