Mishra Sushruta, Jena Lambodar, Mishra Nilamadhab, Chang Hsien-Tsung
School of Computer Engineering, Kalinga Institute of Industrial Technology Deemed to be University, Bhubaneswar, India.
Center for Data Science, Department of Computer Science and Engineering, Siksha 'O' Anusandhan (Deemed to be)University, Bhubaneswar, India.
Heliyon. 2024 Jul 15;10(14):e34593. doi: 10.1016/j.heliyon.2024.e34593. eCollection 2024 Jul 30.
This paper introduces a mobile cloud-based predictive model for assisting Parkinson's disease (PD) patients. PD, a chronic neurodegenerative disorder, impairs motor functions and daily tasks due to the degeneration of dopamine-producing neurons in the brain. The model utilizes smartphones to aid patients in collecting voice samples, which are then sent to a cloud service for storage and processing. A hybrid deep learning model, trained using the UCI Parkinson's Telemonitoring Voice dataset, analyzes this data to estimate the severity of PD symptoms. The model's performance is noteworthy, with accuracy, sensitivity, and specificity metrics of 96.2 %, 94.15 %, and 96.15 %, respectively. Additionally, it boasts a rapid response time of just 13 s. Results are delivered to users via smartphone alert notifications, coupled with a knowledge base feature that educates them about PD. This system provides reliable home-based assessment and monitoring of PD and enables prompt medical intervention, significantly enhancing the quality of life for patients with Parkinson's disease.
本文介绍了一种基于移动云的预测模型，用于辅助帕金森病（PD）患者。帕金森病是一种慢性神经退行性疾病，由于大脑中产生多巴胺的神经元退化，会损害运动功能和日常活动。该模型利用智能手机帮助患者收集语音样本，然后将其发送到云服务进行存储和处理。一个使用UCI帕金森病远程监测语音数据集训练的混合深度学习模型，对这些数据进行分析以估计帕金森病症状的严重程度。该模型的性能值得关注，其准确率、灵敏度和特异性指标分别为96.2%、94.15%和96.15%。此外，它的响应时间仅为13秒，速度很快。结果通过智能手机警报通知发送给用户，并配有一个知识库功能，向他们传授有关帕金森病的知识。该系统提供可靠的基于家庭的帕金森病评估和监测，并能实现及时的医疗干预，显著提高帕金森病患者的生活质量。
