title: Can machine-learning improve cardiovascular risk prediction using routine clinical data?
abstract: BACKGROUND
  Current approaches to predict cardiovascular risk fail to identify many people who would benefit from preventive treatment, while others receive unnecessary intervention. Machine-learning offers opportunity to improve accuracy by exploiting complex interactions between risk factors. We assessed whether machine-learning can improve cardiovascular risk prediction.
  
  
  METHODS
  Prospective cohort study using routine clinical data of 378,256 patients from UK family practices, free from cardiovascular disease at outset. Four machine-learning algorithms (random forest, logistic regression, gradient boosting machines, neural networks) were compared to an established algorithm (American College of Cardiology guidelines) to predict first cardiovascular event over 10-years. Predictive accuracy was assessed by area under the 'receiver operating curve' (AUC); and sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) to predict 7.5% cardiovascular risk (threshold for initiating statins).
  
  
  RESULTS
  24,970 incident cardiovascular events (6.6%) occurred. Compared to the established risk prediction algorithm (AUC 0.728, 95% CI 0.723-0.735), machine-learning algorithms improved prediction: random forest +1.7% (AUC 0.745, 95% CI 0.739-0.750), logistic regression +3.2% (AUC 0.760, 95% CI 0.755-0.766), gradient boosting +3.3% (AUC 0.761, 95% CI 0.755-0.766), neural networks +3.6% (AUC 0.764, 95% CI 0.759-0.769). The highest achieving (neural networks) algorithm predicted 4,998/7,404 cases (sensitivity 67.5%, PPV 18.4%) and 53,458/75,585 non-cases (specificity 70.7%, NPV 95.7%), correctly predicting 355 (+7.6%) more patients who developed cardiovascular disease compared to the established algorithm.
  
  
  CONCLUSIONS
  Machine-learning significantly improves accuracy of cardiovascular risk prediction, increasing the number of patients identified who could benefit from preventive treatment, while avoiding unnecessary treatment of others.
authors.affiliation: NIHR School for Primary Care Research, University of Nottingham, Nottingham, United Kingdom.|Division of Primary Care, School of Medicine, University of Nottingham, Nottingham, United Kingdom.
authors.forename: Stephen F
authors.identifier: http://orcid.org/0000-0002-5281-9590
authors.initials: SF
authors.lastname: Weng
authors.affiliation: Advanced Data Analysis Centre, University of Nottingham, Nottingham, United Kingdom.|School of Computer Science, University of Nottingham, Nottingham, United Kingdom.
authors.forename: Jenna
authors.initials: J
authors.lastname: Reps
authors.affiliation: NIHR School for Primary Care Research, University of Nottingham, Nottingham, United Kingdom.|Division of Primary Care, School of Medicine, University of Nottingham, Nottingham, United Kingdom.
authors.forename: Joe
authors.initials: J
authors.lastname: Kai
authors.affiliation: Advanced Data Analysis Centre, University of Nottingham, Nottingham, United Kingdom.|School of Computer Science, University of Nottingham, Nottingham, United Kingdom.
authors.forename: Jonathan M
authors.initials: JM
authors.lastname: Garibaldi
authors.affiliation: NIHR School for Primary Care Research, University of Nottingham, Nottingham, United Kingdom.|Division of Primary Care, School of Medicine, University of Nottingham, Nottingham, United Kingdom.
authors.forename: Nadeem
authors.initials: N
authors.lastname: Qureshi
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D002318:Cardiovascular Diseases / Q000209:etiology* / Q000517:prevention & control*; D015331:Cohort Studies; D057286:Electronic Health Records / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D016015:Logistic Models; D000069550:Machine Learning; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D011446:Prospective Studies; D012307:Risk Factors