帕金森病数据集
帕金森病患者与健康人群的语音录音数据集，用于基于特征提取的疾病检测。
基本信息
资源简介
该数据集包含语音样本，用于检测帕金森病。数据集包含56名受试者的195个录音，记录了持续元音a和o的发音，提取了抖动、闪烁、NHR、HNR等特征，用于训练支持向量机模型。
下载信息
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暂未开放千方医数集，医疗数据集部分，是为社区服务的公开医疗数据集搜索引擎，并不存储或者下载原始的任何数据。 如果您有其他医疗数据需求，可以和客服联系，或者下工单。我们有强大的三甲医疗机构帮助您提供个性化的医疗数据定制、采集、标注服务。
使用方式
数据集获取
git clone https://github.com/AmanBhagat23/Parkinsons_Disease_Detection_Model.git
curl -L -o repo.zip https://github.com/AmanBhagat23/Parkinsons_Disease_Detection_Model/archive/refs/heads/main.zip
unzip repo.zip
源站 README 摘录（使用方式）
Built a Parkinson’'s Disease Detection Model using Support Vector Machine
Dataset Source : https://www.kaggle.com/datasets/vikasukani/parkinsons-disease-data-set
Method Used
Support vector machine (SVM):
Support Vector Machine (SVM) are a set of supervised learning methods used for classification, regression, and outlier detection. Advantages of support vector machines: Effective for high-dimensional spaces. It is still valid if the number of measurements is greater than the number of samples.
Model Explanation
Parkinson’s disease can be detected using speech. The phonation is the most affected part of the speech i.e. the sound we make when we pronounce the vowels. We have used the database of the speech samples containing the phonation from the affected and healthy people. Speech signals or the voice samples consists of voice samples of people. The samples of healthy people are also collected for the comparative study. The Test Data belongs to 56 subjects. During the collection of this dataset, 48 people are asked to say only the sustained vowels ‘‘a’’ and ‘‘o’’ three times respectively. Total of 195 recordings are obtained from the repository. In the training phase the pre-processing of these signals is done for feature extraction by SVM feature in Scikit-learn. The features extracted are jitter, shimmer, NHR, HNR, mean and median pitch, number of pulses and periods, minimum and maximum period, SD, SD of period, number and degree of voice breaks. All these features differ from patient to patient depending upon the fact how much Parkinson’s disease has progressed. After extracting all the features we will do dimensionality reduction of the features using particle swarm optimization(PSO), In this optimization method it works like swarm particle and reduce the features selection to a minimum, optimization involves in achieving better result in less computation, after selection of the features, The features are used to train the SVM classifier and the model is trained.
完整仓库：github.com/AmanBhagat23/Parkinsons_Disease_Detection_Model
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