Predicting cardiovascular disease by combining optimal feature selection methods with machine learning

Mauricio Rodriguez Segura, Orietta Nicolis, Billy Peralta Marquez, Juan Carrillo Azocar

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Cardiovascular Disease (CVD) is one of the main causes of death in the world. Early detection could prevent deaths associated to cardiac problems. In this work, we propose a methodology based on data pre-processing and Machine Learning (ML) techniques for predicting cardiovascular disease, by using the Sleep Heart Health Study (SHHS) dataset. First, the principal component analysis and lowest p-value logistic regression are applied to select optimal features which could be related to the CVD then, the selected features are used for training four ML algorithms: Naïve Bayes (NB), Feed Forward Neural Networks (NN), Support Vector Machine (SVM) and Random Forest (RF). A binary feature was considered as output of the proposed models and the SMOTE sampling has been used for balancing the training set. Among the proposed methods, NN provided the best accuracy (0.81) and AUC (0.76) outperforming the results obtained in other studies.

Original languageEnglish
Title of host publication2020 39th International Conference of the Chilean Computer Science Society, SCCC 2020
PublisherIEEE Computer Society
ISBN (Electronic)9781728183282
DOIs
Publication statusPublished - 16 Nov 2020
Event39th International Conference of the Chilean Computer Science Society, SCCC 2020 - Coquimbo, Chile
Duration: 16 Nov 202020 Nov 2020

Publication series

NameProceedings - International Conference of the Chilean Computer Science Society, SCCC
Volume2020-November
ISSN (Print)1522-4902

Conference

Conference39th International Conference of the Chilean Computer Science Society, SCCC 2020
Country/TerritoryChile
CityCoquimbo
Period16/11/2020/11/20

Keywords

  • Cardiovascular disease
  • classification models
  • linear regression
  • PCA

ASJC Scopus subject areas

  • Engineering(all)
  • Computer Science(all)

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