Mian Tariq Saeed
Department of IS, College of Computer Science and Engineering, Taibah University, Madinah Al Munawara 43353, Saudi Arabia.
Diagnostics (Basel). 2022 Jul 25;12(8):1796. doi: 10.3390/diagnostics12081796.
Parkinson's disease (PD) is the second most common neurodegenerative disorder after Alzheimer's disease. It has a slow progressing neurodegenerative disorder rate. PD patients have multiple motor and non-motor symptoms, including vocal impairment, which is one of the main symptoms. The identification of PD based on vocal disorders is at the forefront of research. In this paper, an experimental study is performed on an open source Kaggle PD speech dataset and novel comparative techniques were employed to identify PD. We proposed an unsupervised autoencoder feature selection technique, and passed the compressed features to supervised machine-learning (ML) algorithms. We also investigated the state-of-the-art deep learning 1D convolutional neural network (CNN-1D) for PD classification. In this study, the proposed algorithms are support vector machine, logistic regression, random forest, naïve Bayes, and CNN-1D. The classifier performance is evaluated in terms of accuracy score, precision, recall, and F1 score measure. The proposed 1D-CNN model shows the highest result of 0.927%, and logistic regression shows 0.922% on the benchmark dataset in terms of F1 measure. The major contribution of the proposed approach is that unsupervised neural network feature selection has not previously been investigated in Parkinson's detection. Clinicians can use these techniques to analyze the symptoms presented by patients and, based on the results of the above algorithms, can diagnose the disease at an early stage, which will allow for improved future treatment and care.
帕金森病（PD）是仅次于阿尔茨海默病的第二常见神经退行性疾病。其神经退行性疾病进展缓慢。帕金森病患者有多种运动和非运动症状，包括嗓音障碍，这是主要症状之一。基于嗓音障碍识别帕金森病是研究的前沿领域。本文对一个开源的Kaggle帕金森病语音数据集进行了实验研究，并采用了新颖的比较技术来识别帕金森病。我们提出了一种无监督自动编码器特征选择技术，并将压缩后的特征传递给监督式机器学习（ML）算法。我们还研究了用于帕金森病分类的前沿深度学习一维卷积神经网络（CNN-1D）。在本研究中，所提出的算法有支持向量机、逻辑回归、随机森林、朴素贝叶斯和CNN-1D。分类器性能通过准确率、精确率、召回率和F1分数度量来评估。所提出的一维卷积神经网络模型在F1度量方面在基准数据集上显示出最高结果为0.927%，逻辑回归显示为0.922%。所提出方法的主要贡献在于，此前在帕金森病检测中尚未研究过无监督神经网络特征选择。临床医生可以使用这些技术来分析患者呈现的症状，并根据上述算法的结果在早期诊断疾病，这将有助于改善未来的治疗和护理。
