心脏疾病数据集
用于预测心脏疾病的结构化表格数据集，包含年龄、性别、胸痛类型等特征，以及是否患有心脏疾病的二分类标签。
基本信息
资源简介
该数据集用于预测用户是否会被诊断出心脏疾病，包含个人健康相关的结构化特征（如年龄、性别、胸痛类型等），以及一个表示是否有心脏疾病的二分类目标变量。数据来源于1988年的四个数据库（Cleveland、Hungary、Switzerland、Long Beach V），适用于心脏疾病分类模型的训练和评估。
下载信息
注册下载
Tips: 该数据集需要在对应的数据源网站注册通过后，才能进行数据下载，注册有对应要求，或者需要收费。
暂未开放公开下载
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使用方式
数据集获取
git clone https://github.com/kevJ711/Machine-Learning-Health-Detection-.git
curl -L -o repo.zip https://github.com/kevJ711/Machine-Learning-Health-Detection-/archive/refs/heads/master.zip
unzip repo.zip
源站 README 摘录（使用方式）
Machine-Learning-Health-Detection-
Dectects if someone has a Heart Disease with python using the Public Health Dataset of heart diseases.
Research and Poster Presentation:
I recently presented a poster on this machine learning model project at the MICRO NANO TECHNOLOGY EDUCATION SPECIAL INTEREST GROUP program. The presentation was well-received, and if you’'re interested in learning more about my findings, I encourage you to check out the detailed poster.
https://www.mntesig.net/mntesig-2023-virtual.html
My Machine Learning Model that predicts if a user would be diagnosed with heart disease based on the Public Health Dataset via https://www.kaggle.com/. This trained machine learning model utilized both the random forest and the naive Bayes classifier to test its accuracy in predicting if an individual would have heart disease.
Primary goal:
The primary goal is to develop a predictive model to determine the likelihood of a user being diagnosed with heart disease based on relevant health-related features.
Dataset Source:
The model is trained on a public health dataset sourced from heart.csv. This dataset comprises hundreds of instances related to individuals’’ health. This data set dates from 1988 and consists of four databases: Cleveland, Hungary, Switzerland, and Long Beach V.
The dataset includes features such as age
sex
chest pain type (4 values)
resting blood pressure
serum cholestoral in mg/dl
fasting blood sugar > 120 mg/dl
resting electrocardiographic results (values 0,1,2)
maximum heart rate achieved
exercise induced angina
oldpeak = ST depression induced by exercise relative to rest
the slope of the peak exercise ST segment
number of major vessels (0-3) colored by flourosopy
thal: 0 = normal; 1 = fixed defect; 2 = reversable defect The names and social security numbers of the patients were recently removed from the database, replaced with dummy values.
The target variable is the diagnosis of heart disease, represented as 0 or 1, where 0 indicates no heart disease and 1 indicates the presence of heart disease.
Model Selection and Training:
The Random Forest Classifier algorithm was selected for its suitability in binary classification tasks. In the updated version of this model, I used the Naive Bayes classifier to test each classifier’'s accuracy.
The 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))
精度瓶颈？数据缺失？
当前公开数据无法满足您的算法精度？千方提供针对 心脏病 的高质量、多模态真实临床数据定制解决方案。
