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 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 ...
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...
Feature extraction using the Bag of Words methods to the correct data and discontinuities collect the heart disease-based matching data's extraction from the dataset and the various combination of the data set is available in Kaggle. It is mostly used for text classification ...
3.1. Dataset The BRFSS 2015 [3] dataset is large volume of dataset, which till date few previous works had used for their research. A total of 253 681 patient records with 18 attributes are considered. The cleaned dataset was downloaded from Kaggle repository and is mostly used for binary ...
and obtaining high prediction accuracy31. The significant drawback of NB in the context of heart disease prediction is that it treats each feature of the dataset individually when calculating probabilities. Therefore, conventional classifiers lead to an incorrect decision support system32. According to ...
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, and evolutionary were used. Then seven algorithms Ba...
Further and somewhat unexpectedly, some variables that are clearly predictive of coronary disease, such as blood levels of LDL-cholesterol, were not included in the dataset; a much more precise model could be obtained by using more information of risk factors of the participants. The possibility ...
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