1、数据集简介 heart disease数据集的下载 数据集下载地址: https://www.kaggle.com/datasets/kamilpytlak/personal-key-indicators-of-heart-disease heart disease数据集的使用方法
kaggle项目---Heart Disease 主要利用了随机森林来训练模型,同时分析了各个属性的权重及对预测结构的影响 importnumpyasnpimportpandasaspdimportmatplotlib.pyplotaspltimportseabornassns#for plottingfromsklearn.ensembleimportRandomForestClassifier#for the modelfromsklearn.treeimportDecisionTreeClassifierfromsklearn.treeim...
The significance of the proposed model is checked by utilizing the cross-validation technique using the heart disease dataset. The proposed model based on ET-CNN successfully categorizes patient data, achieving 0.960, an average accuracy. Additionally, the recall, precision, and F1 scores for the ...
Kaggle自己贴出了两种解决方案,一种是基于傅里叶分析的方法,另外一种是在Caffe平台上实现的基于全卷积神经网络的方法。我只稍微看了一下第二种,它在使用NDSB2的数据集进行训练之前先找了一个Sunnybrook dataset来进行训练,相当于增大了数据集,效果应该是提升了不少。 扫码后在手机中选择通过第三方浏览器下载...
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 found that using a heart disease dataset collected from Kaggle three-classification based on k-nearest neighbor (KNN), decision tree (DT) and random forests (RF) algorithms the RF method achieved 100% accuracy along with 100% sensitivity and specificity. Thus, we found that a ...
However, in order to discover the optimal HDP solution, 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 ...
In this paper, we proposed a novel machine learning model for heart disease prediction. The proposed method was tested on two different datasets from Kaggle and UCI. We applied sampling techniques to the unbalanced dataset and feature selection techniques are used to find the best features. Later...
Looking at the significance of the dataset, two datasets i.e. 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 ...
Coronary Heart Disease Prediction About the dataset: The "Framingham" dataset is publically available on the Kaggle website, and it is from an ongoing cardiovascular study on residents of the town of Framingham, Massachusetts. The classification goal is to predict whether the patient has 10-year ...