N. R, Vinora A, S AlwynRajiv, Ajitha E, Sivakarthi G
{"title":"利用机器学习检测帕金森病","authors":"N. R, Vinora A, S AlwynRajiv, Ajitha E, Sivakarthi G","doi":"10.1109/ICECONF57129.2023.10083581","DOIUrl":null,"url":null,"abstract":"SubstantiaNigra, a part of the Mesencephalon, is the primary site of dopaminergic neuron loss in Parkinson's disease (PD), which is currently incurable (midbrain). All PD medications address symptoms; they do not prevent or postpone the degeneration of dopaminergic neurons. Machine learning techniques are applicable here since tremor detection and classification are crucial for the diagnosis and treatment of PD patients. This is one of the most common movement disorders seen in clinical practice, and it is frequently categorized according to etiological or behavioral traits. Another critical challenge is identifying and evaluating PD-related gait patterns, such as gait start and freezing, which are defining PD symptoms. Given that 90% of Parkinson's disease patients have vocal impairment, it is crucial to analyze speech data to discriminate between healthy individuals and those with the condition. This study, which presents a succinct assessment of the current state-of-the-art and some potential for future research, was inspired by the ongoing PD management initiative.","PeriodicalId":436733,"journal":{"name":"2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Detecting Parkinson's Disease using Machine Learning\",\"authors\":\"N. R, Vinora A, S AlwynRajiv, Ajitha E, Sivakarthi G\",\"doi\":\"10.1109/ICECONF57129.2023.10083581\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"SubstantiaNigra, a part of the Mesencephalon, is the primary site of dopaminergic neuron loss in Parkinson's disease (PD), which is currently incurable (midbrain). All PD medications address symptoms; they do not prevent or postpone the degeneration of dopaminergic neurons. Machine learning techniques are applicable here since tremor detection and classification are crucial for the diagnosis and treatment of PD patients. This is one of the most common movement disorders seen in clinical practice, and it is frequently categorized according to etiological or behavioral traits. Another critical challenge is identifying and evaluating PD-related gait patterns, such as gait start and freezing, which are defining PD symptoms. Given that 90% of Parkinson's disease patients have vocal impairment, it is crucial to analyze speech data to discriminate between healthy individuals and those with the condition. This study, which presents a succinct assessment of the current state-of-the-art and some potential for future research, was inspired by the ongoing PD management initiative.\",\"PeriodicalId\":436733,\"journal\":{\"name\":\"2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)\",\"volume\":\"46 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-01-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICECONF57129.2023.10083581\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICECONF57129.2023.10083581","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Detecting Parkinson's Disease using Machine Learning
SubstantiaNigra, a part of the Mesencephalon, is the primary site of dopaminergic neuron loss in Parkinson's disease (PD), which is currently incurable (midbrain). All PD medications address symptoms; they do not prevent or postpone the degeneration of dopaminergic neurons. Machine learning techniques are applicable here since tremor detection and classification are crucial for the diagnosis and treatment of PD patients. This is one of the most common movement disorders seen in clinical practice, and it is frequently categorized according to etiological or behavioral traits. Another critical challenge is identifying and evaluating PD-related gait patterns, such as gait start and freezing, which are defining PD symptoms. Given that 90% of Parkinson's disease patients have vocal impairment, it is crucial to analyze speech data to discriminate between healthy individuals and those with the condition. This study, which presents a succinct assessment of the current state-of-the-art and some potential for future research, was inspired by the ongoing PD management initiative.