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Comparison of machine learning algorithms for clinical event prediction (risk of coronary heart disease)

Juan-Jose Beunza1, Enrique Puertas2, Ester García-Ovejero3

  • 1Machine Learning Health Working Group, Faculty of Biomedical and Health Sciences, Universidad Europea de Madrid, Madrid, Spain; Department of Medicine, Faculty of Biomedical and Health Sciences, Universidad Europea de Madrid, Madrid, Spain.

Journal of Biomedical Informatics
|August 3, 2019
PubMed

Insights

Machine learning algorithms can improve clinical event prediction accuracy. Performance varied based on the software platform used for analysis, with neural networks and support vector machines showing the best results.

Area of Science:

  • Cardiovascular disease research
  • Biostatistics
  • Machine learning applications in healthcare

Background:

  • The Framingham Heart Study, initiated in 1948, provides a long-term dataset for cardiovascular disease risk factor research.
  • Supervised machine learning (ML) algorithms offer potential for enhancing diagnostic and prognostic capabilities beyond traditional methods.

Purpose of the Study:

  • To compare the internal validity and predictive accuracy of various supervised ML algorithms.
  • To evaluate the influence of different statistical software platforms on ML algorithm performance.

Main Methods:

  • Utilized the Framingham Heart Study open database (4240 observations).
  • Compared ML algorithms including decision tree, random forest, support vector machines, neural networks, and logistic regression.
  • Employed R-Studio and RapidMiner for data analysis, with Area Under the Curve (AUC) as the primary selection criterion.

Main Results:

  • In R-Studio, a neural network model excluding missing values achieved the highest AUC (0.71).
  • In RapidMiner, support vector machines on the same model yielded the best AUC (0.75).

Conclusions:

  • ML algorithms can augment the diagnostic and prognostic power of traditional regression models.
  • The choice of statistical software platform significantly impacts the performance and applicability of ML algorithms in clinical event prediction.
Abstract

Keywords:
Area under curveDiagnostic techniques and proceduresMachine learningResearch techniquesSupervised machine learningSupport vector machines

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