Predicting Survival of People with Heart Failure Using Oversampling, Feature Selections and Dimensionality Reduction
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Date
2022
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical and Electronics Engineers Inc.
Abstract
Cardiovascular diseases are deadly and kill millions of people around the world every year. Heart failure is one of the unfortunate consequence where the heart is unable to pump enough blood for the body. A medical checkup of these patients with attributes including creatinine phosphokinase, ejection fraction, serum creatinine and serum sodium can be used for analysis. In this paper, we have analysed this clinical data and built machine learning models that can predict the survival rate of heart failure of a person. We have used various dimensionality reduction techniques to analyse the data with the aim of reducing the dimensions of the dataset. Finally, we reduced the overfitting of data using Synthetic Minority Oversampling Technique(SMOTE) and Adaptive Synthetic(ADASYN). © 2022 IEEE.
Description
Keywords
Adaptive Synthetic, Dimensionality reduction, Independent Component Analysis, Isometric Mapping, Principal Component Analysis, Sample Minority Oversampling Technique, Synthetic Minority Oversampling Technique, T-Stochastic Neighbourhood Embedding, Uniform Manifold Approximation and Projection
Citation
7th IEEE International Conference on Recent Advances and Innovations in Engineering, ICRAIE 2022 - Proceedings, 2022, Vol., , p. 347-354