Dao Son V T, Yu Zhiqiu, Tran Ly V, Phan Phuc N K, Huynh Tri T M, Le Tuan M
School of Industrial Engineering and Management, International University, Vietnam National University, Ho Chi Minh City 700000, Vietnam.
Department of Industrial Management, National Taiwan University of Science and Technology, Taipei City 106, Taiwan.
Diagnostics (Basel). 2022 Aug 16;12(8):1980. doi: 10.3390/diagnostics12081980.
Parkinson's Disease (PD) is a brain disorder that causes uncontrollable movements. According to estimation, roughly ten million individuals worldwide have had or are developing PD. This disorder can have severe consequences that affect the patient's daily life. Therefore, several previous works have worked on PD detection. Automatic Parkinson's Disease detection in voice recordings can be an innovation compared to other costly methods of ruling out examinations since the nature of this disease is unpredictable and non-curable. Analyzing the collected vocal records will detect essential patterns, and timely recommendations on appropriate treatments will be extremely helpful. This research proposed a machine learning-based approach for classifying healthy people from people with the disease utilizing Grey Wolf Optimization (GWO) for feature selection, along with Light Gradient Boosted Machine (LGBM) to optimize the model performance. The proposed method shows highly competitive results and has the ability to be developed further and implemented in a real-world setting.
帕金森病（PD）是一种导致无法控制的运动的脑部疾病。据估计，全球约有一千万人已经患有或正在患上帕金森病。这种疾病会产生严重后果，影响患者的日常生活。因此，先前已有多项研究致力于帕金森病的检测。与其他昂贵的排除性检查方法相比，通过语音记录自动检测帕金森病可能是一项创新，因为这种疾病的性质不可预测且无法治愈。分析收集到的语音记录将检测到关键模式，及时提供适当治疗的建议将非常有帮助。本研究提出了一种基于机器学习的方法，利用灰狼优化算法（GWO）进行特征选择，将健康人与患病人士进行分类，同时使用轻量级梯度提升机（LGBM）来优化模型性能。所提出的方法显示出极具竞争力的结果，并且有进一步发展并在实际环境中实施的能力。
