帕金森病语音数据集
包含195个帕金森病语音特征样本的表格数据集，用于分类检测。
基本信息
资源简介
帕金森病语音数据集
【数据集背景】
本数据集源站标题为《Aadi-J/Parkinsons-Detection》。源站首次发布：2023-12-17。
【数据内容】
源站原始描述：This project leverages machine learning for early Parkinson''s diagnosis via speech analysis. Using a dataset from the University of Oxford, our models achieved 98% accuracy, outperforming conventional methods. Conducted in March 2022, this research provides a promising alternative for accurate and early Parkinson''s detection. # A Machine Learning Approach for the Diagnosis of Parkinson''s Disease via Speech Analysis ## Introduction This project, researched in March 2022, aims to provide a machine…
【适用方向】
结构化表格数据可直接用于统计建模、特征工程与队列分析。
【获取与许可】
源站页面：https://github.com/Aadi-J/Parkinsons-Detection
使用许可：MIT。
下载信息
注册下载
需要注册 Kaggle 账号并登录后下载，适合需要跟踪下载记录和使用 API 的用户。
暂未开放公开下载
无需注册即可直接获取公开样本或文档，适合快速预览和评估数据集质量。
免登录有偿下载
公开数据集受托下载与技术交付服务。
提供高速下载与技术交付服务（收技术服务费，非数据销售）
暂未开放当前数据集主要来源为 Kaggle 公开托管，完整影像包建议通过原始链接或 Kaggle API 获取。
使用方式
数据集获取
git clone https://github.com/Aadi-J/Parkinsons-Detection.git
curl -L -o repo.zip https://github.com/Aadi-J/Parkinsons-Detection/archive/refs/heads/main.zip
unzip repo.zip
源站 README 摘录（使用方式）
A Machine Learning Approach for the Diagnosis of Parkinson’'s Disease via Speech Analysis
Introduction
This project, researched in March 2022, aims to provide a machine learning-based approach for accurately diagnosing Parkinson’'s Disease using speech analysis. Due to the deletion of the initial account, the project was reuploaded.
Parkinson’s Disease, the second most prevalent neurodegenerative disorder globally, affects over 10 million people. The current diagnostic methods are only 53% accurate for early diagnosis (within 5 years of symptoms). This project explores a machine learning approach using a dataset from the University of Oxford, focusing on various speech features.
Background
Parkinson’'s Disease
Parkinson’s is characterized by the death of dopamine-containing cells in the substantia nigra, impacting motor and cognitive abilities. Symptoms include frozen facial features, slowness of movement, tremors, and voice impairment.
Performance Metrics
- Accuracy: (TP+TN)/(P+N)
- Matthews Correlation Coefficient: 1=perfect, 0=random, -1=completely inaccurate
Algorithms Employed
- Logistic Regression (LR): Uses the sigmoid logistic equation.
- Linear Discriminant Analysis (LDA): Assumes Gaussian data with the same variance.
- k Nearest Neighbors (KNN): Predictions based on the k closest instances.
- Decision Tree (DT): Binary tree structure for predictions.
- Neural Network (NN): Models the human brain’'s decision-making.
- Naive Bayes (NB): Assumes independence between features.
- Gradient Boost (GB): Combines weak learners to create a strong learner.
Engineering Goal
Produce a machine learning model for Parkinson’s diagnosis with at least 90% accuracy and/or a Matthews Correlation Coefficient of at least 0.9. Compare algorithms and parameters to determine the best model.
Dataset Description
- Source: University of Oxford
- 195 instances: 147 Parkinson’‘s subjects, 48 without Parkinson’'s
- 22 features: Characteristics like frequency, pitch, amplitude/period of the sound wave
- 1 label: 1 for Parkinson’s, 0 for no Parkinson’s
Summary of Procedure
- Split dataset into training and validation sets.
- Train each algorithm: LR, LDA, KNN, DT, NN, NB, GB.
- Evaluate results using the validation set.
- Repeat for different training/validation splits and a rescaled dataset.
数据加载示例（表格/文本类）
import pandas as pd, glob, os
files = (glob.glob(os.path.join(path, "**", "*.csv"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.tsv"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.xlsx"), recursive=True))
print("数据文件:", files)
df = pd.read_csv(files[0])
print(df.shape); print(df.columns.tolist()); print(df.head(3))
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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