1 数据和准备工作 数据下载地址:Heart Disease UCI,下载下来的是一个CSV格式的数据,一共1000行,14列。前13列是特征(变量),最后一列是Y(患心脏病=1,否=0),target。 我们先在Google Sheets中查看下数据。 准备工作,数据导入。 ## ### Python 3.8 @Jupyter Notebook, Spyder## Author: Wangjixing, brycew...
heart_disease_prediction 心脏病UCI数据集 该实验只是根据心脏病的缺席情况简单地预测心脏病的存在。 1.关于数据集: 该数据集在Kaggle( )上提供。 并且可以从UCI机器学习存储库( )中获得。 数据包含总共14个属性,如下所示。 属性说明 年龄:岁 性别:性别(1 =男性; 0 =女性)...
this is the Jupiter notebook code and I've used dataset from kaggle.com and UCI repository for various diseases-based datasets. I've used a variety of Machine Learning algorithms, implemented in Python, to predict the presence of heart disease in a patient. This is a classification problem,...
Heart disease is one of the primary reasons for mortality worldwide. With the use of Artificial Intelligence (AI) approaches, it is possible to monitor certain characteristics such as blood pressure, body weight, cholesterol, sugar level, and heart rate to determine cardiac disease in its initial...
UCI_MLRepository is the cardiac disease-related publicly available dataset99. Every clinical case out of 303 includes a target attribute among a total of 76 features. The target attribute is represented by an integer ranging from 0 to 4, where 0 indicates a heart patient and values in the ...
heart_disease_prediction 心脏病UCI数据集 该实验只是根据心脏病的缺席情况简单地预测心脏病的存在。 1.关于数据集: 该数据集在Kaggle( )上提供。 并且可以从UCI机器学习存储库( )中获得。 数据包含总共14个属性,如下所示。 属性说明 年龄:岁 性别:性别(1 =男性; 0 =女性) cp:胸痛类型 值1:典型心绞痛值2...
A summary of related research in cardiovascular disease diagnosis is presented in Table 1. Table 1. Summary of Research related to Cardiovascular Disease Diagnosis. ScholarDatasetYearModelPerformanceTechnology Ahmad et al. [11] Hungary, Switzerland &; Long Beach V and UCI Kaggle 2022 XGBoost accuracy...
the Cleveland heart disease dataset S1 and Hungarian heart disease dataset (S2) are used, which are available online at the University of California Irvine (UCI) machine learning repository and UCI Kaggle repository, and various researchers have used it for conducting their research studies28,31,32...
The present study examines the role of feature selection methods in optimizing machine learning algorithms for predicting heart disease. The Cleveland Heart disease dataset with sixteen feature selection techniques in three categories of filter, wrapper,
this study compares multiple classification algorithms utilizing two separate heart disease datasets from the Kaggle repository and the University of California,Irvine(UCI)machine learning repository.In a comparative analysis,Mean Absolute Error(MAE),Relative Absolute Error(RAE),precision,recall,fmeasure,and...