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 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...
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...
Grid search optimisation significantly enhances machine learning models' performance in predicting cardiovascular diseases by systematically identifying optimal parameter combinations. Using the IELBT method, Bizimana et al. [18] achieved an accuracy rate of 96.00 % on the Alizadeh Sani HD Dataset. ...
Svetlana ulianova Cardiovascular disease dataset Retrieved from 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...
[20]https://www.kaggle.com/johnsmith88/heart-disease-dataset [Accessed 02 June 2021]. [21]Azur MJ, Stuart EA, Frangakis C, Leaf PJ. (2011). Multiple imputation by chained equations: what is it and how does it work. Int J Methods Psychiatr Res 20(1): 40-49. ...
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,
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 ...
Electrocardiogram (ECG) signals represent the electrical activity of the human hearts and consist of several waveforms (P, QRS, and T). The duration and shape of each waveform and the distances between different peaks are used to diagnose heart diseases.
Wrapper, Filter, and Embedded methods are analyzed and implemented using the Kaggle heart disease dataset in Python to find the major risk factor of heart disease. The objective of this research article is to find the major risk factor for heart disease. From the implementation of feature ...