Predicting Survival of People with Heart Failure Using Oversampling, Feature Selections and Dimensionality Reduction

Simple item page
dc.contributor.authorNiharika, G.
dc.contributor.authorLekha, A.I.
dc.contributor.authorLeela Akshaya, T.
dc.contributor.authorAnand Kumar, A.M.
dc.date.accessioned2026-02-06T06:35:18Z
dc.date.issued2022
dc.description.abstractCardiovascular 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.
dc.identifier.citation7th IEEE International Conference on Recent Advances and Innovations in Engineering, ICRAIE 2022 - Proceedings, 2022, Vol., , p. 347-354
dc.identifier.urihttps://doi.org/10.1109/ICRAIE56454.2022.10054296
dc.identifier.urihttps://idr.nitk.ac.in/handle/123456789/29772
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.subjectAdaptive Synthetic
dc.subjectDimensionality reduction
dc.subjectIndependent Component Analysis
dc.subjectIsometric Mapping
dc.subjectPrincipal Component Analysis
dc.subjectSample Minority Oversampling Technique
dc.subjectSynthetic Minority Oversampling Technique
dc.subjectT-Stochastic Neighbourhood Embedding
dc.subjectUniform Manifold Approximation and Projection
dc.titlePredicting Survival of People with Heart Failure Using Oversampling, Feature Selections and Dimensionality Reduction

Files

Collections

Conference Papers