With proper analysis of certain attributes, stroke can be predicted well in time before the occurrence, and life may be saved. One such attempt has been made in this paper using machine learning algorithms. A dataset was acquired from Kaggle. The attributes provided in the dataset were analyzed...
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 相关文章
Heart disease is a prominent cause of death globally, and effective prediction of heart disease can considerably improve patient outcomes15. In this work, we suggest using a Self-Attention-based Transformer Model to improve heart disease prediction. We make use of the Cleveland dataset36, a freque...
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
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,
https://www.kaggle.com/sulianova/cardiovascular-disease-dataset (2019, january 01) Google Scholar [8] C.B. Latha, S.C. Jeeva Improving the accuracy of prediction of heart disease risk based on ensemble classification techniques July 02 https://www.sciencedirect.com/science/article/pii/S2352914...
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 ...
The dataset generated and analyzed during the current study are available in the Kaggle repository, (https://www.kaggle.com/datasets/mohamedamineferrag/edgeiiotset-cyber-security-dataset-of-iot-iiot). The dataset analyzed during the current study is available from the corresponding author upon rea...
Kaggle Inc; Publicado y actualizado 7 nov 2017. URL <https://www.kaggle.com/amanajmera1/framingham-heart-study-dataset> (accessed 8 mar 2019), Versión 1. Google Scholar [17] J.J. Beunza, R-studio code for Machine Learning algorithms applied to 10-year coronary risk in the Framingham ...
The algorithms are applied to a dataset taken from the Kaggle site including 70000 samples. In algorithms, methods such as the importance of features, hold out validation, 10-fold cross-validation, stratified 10-fold cross-validation, leave one out cross-validation are the resu...