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
帕金森病数据集
【数据集背景】
本数据集源站标题为《AmanBhagat23/Parkinsons_Disease_Detection_Model》。源站首次发布：2024-04-23。
【数据内容】
源站原始描述：In this Machine Learning Project, using the Python libraries scikit-learn, numpy, and pandas. I have build a model using an SVM. We’ll load the data, get the features and labels, scale the features, then split the dataset, build an SVM model, and then calculate the accuracy of our model. # Built a Parkinson''s Disease Detection Model using Support Vector Machine Dataset Source : # Method Used Support vector machine (SVM): Support Vector Machine (SVM) are a set of supervised learning methods used…
【适用方向】
音频数据可用于声学信号分析建模，如语音与呼吸音相关研究。
【获取与许可】
源站页面：https://github.com/AmanBhagat23/Parkinsons_Disease_Detection_Model
下载信息
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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
数据缺失？
依托陕西、四川两大基地，我们与超过十家三甲医院建立直接合作关系，覆盖合规授权、采集治理、专业标注、数据交付的全流程，为AI医疗团队提供即拿即用的高质量临床数据。
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