Machine Learning for Real-Time Heart Disease Prediction

Dimitris Bertsimas, Luca Mingardi, Bartolomeo Stellato

Research output: Contribution to journalArticlepeer-review

Abstract

Heart-related anomalies are among the most common causes of death worldwide. Patients are often asymptomatic until a fatal event happens, and even when they are under observation, trained personnel is needed in order to identify a heart anomaly. In the last decades, there has been increasing evidence of how Machine Learning can be leveraged to detect such anomalies, thanks to the availability of Electrocardiograms (ECG) in digital format. New developments in technology have allowed to exploit such data to build models able to analyze the patterns in the occurrence of heart beats, and spot anomalies from them. In this work, we propose a novel methodology to extract ECG-related features and predict the type of ECG recorded in real time (less than 30 milliseconds). Our models leverage a collection of almost 40 thousand ECGs labeled by expert cardiologists across different hospitals and countries, and are able to detect 7 types of signals: Normal, AF, Tachycardia, Bradycardia, Arrhythmia, Other or Noisy. We exploit the XGBoost algorithm, a leading machine learning method, to train models achieving out of sample F1 Scores in the range 0.93 0.99. To our knowledge, this is the first work reporting high performance across hospitals, countries and recording standards.

Original languageEnglish (US)
JournalIEEE Journal of Biomedical and Health Informatics
DOIs
StateAccepted/In press - 2021

All Science Journal Classification (ASJC) codes

  • Biotechnology
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Health Information Management

Keywords

  • Arrhythmia
  • Boosting
  • Data models
  • Diseases
  • ECG
  • Electrocardiography
  • Feature extraction
  • Heart
  • Machine Learning
  • Real-time systems
  • Time series analysis

Fingerprint

Dive into the research topics of 'Machine Learning for Real-Time Heart Disease Prediction'. Together they form a unique fingerprint.

Cite this