超声心动图数据集
该数据集包含心脏病患者的医疗记录，数据模态为临床表格，涵盖生存时间、是否存活、年龄、心脏状况等多项指标，主要用于预测患者是否能在心脏病发作后至少存活两年的研究，支持心脏病生存预测和临床风险评估等应用。
基本信息
资源简介
超声心动图数据集
【数据集背景】
本数据集源站标题为《myamiri/Analysis-for-Echocardiogram-Dataset》。源站首次发布：2020-09-23。
【数据内容】
源站原始描述：# Analysis-for-Echocardiogram-Dataset # Problem Predict whether a patient (has) survived at least 2 years # Solution I used two methods: - Random Forest - Logistic Regression # Data All the patients suffered heart attacks at some point in the past. Some are still alive and some are not. The survival and still-alive variables, when taken together, indicate whether a patient survived for at least one year following the heart attack. - survival – the number of months patient survived (has survived,…
【获取与许可】
源站页面：https://github.com/myamiri/Analysis-for-Echocardiogram-Dataset
下载信息
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使用方式
数据集获取
git clone https://github.com/myamiri/Analysis-for-Echocardiogram-Dataset.git
curl -L -o repo.zip https://github.com/myamiri/Analysis-for-Echocardiogram-Dataset/archive/refs/heads/master.zip
unzip repo.zip
源站 README 摘录（使用方式）
Analysis-for-Echocardiogram-Dataset
Problem
Predict whether a patient (has) survived at least 2 years
Solution
I used two methods:
- Random Forest
- Logistic Regression
Data
All the patients suffered heart attacks at some point in the past. Some are still alive and some are not. The survival and still-alive variables, when taken together, indicate whether a patient survived for at least one year following the heart attack.
- survival – the number of months patient survived (has survived, if patient is still alive). Because all the patients had their heart attacks at different times,
it is possible that some patients have survived less than one year but they are still alive. Check the second variable to confirm this. Such patients cannot be used for the prediction task mentioned above. - still-alive – a binary variable. 0=dead at end of survival period, 1 means still alive
- age-at-heart-attack – age in years when heart attack occurred
- pericardial-effusion – binary. Pericardial effusion is fluid around the heart. 0=no fluid, 1=fluid
- fractional-shortening – a measure of contracility around the heart lower numbers are increasingly abnormal
- epss – E-point septal separation, another measure of contractility. Larger numbers are increasingly abnormal.
- lvdd – left ventricular end-diastolic dimension. This is a measure of the size of the heart at end-diastole. Large hearts tend to be sick hearts.
- wall-motion-score – a measure of how the segments of the left ventricle are moving
- wall-motion-index – equals wall-motion-score divided by number of segments seen
- Mult-A derivate variable (suggested that it can be ignored)
- Name-The Name of the patient
- Group-Group (Has been considered meaningless and suggested to ignore)
- alive-at-1 – Boolean-valued. Derived from the first two attributes. 0 means patient was either dead after 1 year or had been followed for less than 1 year. 1 means patient was alive at 1 year.
Modeling
I used Random Forest that is a bagging model to avoid overfitting which is a very common problem in case we don’t have enough samples.
Random forest trains several Decision Trees in parallel by using a random subset of samples and features with replacement and considers
the average performance of the all Decision Trees as the final performance of the model.
Also, I used Logistic Regression whic
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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