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

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

Resultado de la investigación: Contribución a los tipos de informe/libroContribución a la conferenciarevisión exhaustiva

Resumen

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.

Idioma originalInglés
Título de la publicación alojada2020 39th International Conference of the Chilean Computer Science Society, SCCC 2020
EditorialIEEE Computer Society
ISBN (versión digital)9781728183282
DOI
EstadoPublicada - 16 nov 2020
Evento39th International Conference of the Chilean Computer Science Society, SCCC 2020 - Coquimbo, Chile
Duración: 16 nov 202020 nov 2020

Serie de la publicación

NombreProceedings - International Conference of the Chilean Computer Science Society, SCCC
Volumen2020-November
ISSN (versión impresa)1522-4902

Conferencia

Conferencia39th International Conference of the Chilean Computer Science Society, SCCC 2020
País/TerritorioChile
CiudadCoquimbo
Período16/11/2020/11/20

Áreas temáticas de ASJC Scopus

  • Ingeniería (todo)
  • Informática (todo)

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