The use of machine learning for the identification of peripheral artery disease and future mortality risk.
OBJECTIVE
A key aspect of the precision medicine effort is the development of informatics tools that can analyze and interpret "big data" sets in an automated and adaptive fashion while providing accurate and actionable clinical information. The aims of this study were to develop machine learning algorithms for the identification of disease and the prognostication of mortality risk and to determine whether such models perform better than classical statistical analyses.


METHODS
Focusing on peripheral artery disease (PAD), patient data were derived from a prospective, observational study of 1755 patients who presented for elective coronary angiography. We employed multiple supervised machine learning algorithms and used diverse clinical, demographic, imaging, and genomic information in a hypothesis-free manner to build models that could identify patients with PAD and predict future mortality. Comparison was made to standard stepwise linear regression models.


RESULTS
Our machine-learned models outperformed stepwise logistic regression models both for the identification of patients with PAD (area under the curve, 0.87 vs 0.76, respectively; P = .03) and for the prediction of future mortality (area under the curve, 0.76 vs 0.65, respectively; P = .10). Both machine-learned models were markedly better calibrated than the stepwise logistic regression models, thus providing more accurate disease and mortality risk estimates.


CONCLUSIONS
Machine learning approaches can produce more accurate disease classification and prediction models. These tools may prove clinically useful for the automated identification of patients with highly morbid diseases for which aggressive risk factor management can improve outcomes.
Division of Vascular Surgery, Stanford Health Care, Stanford, Calif.
Elsie Gyang
EG
Ross
Center for Biomedical Informatics Research, Stanford University, Stanford, Calif.
Nigam H
NH
Shah
Division of Vascular Surgery, Stanford Health Care, Stanford, Calif.
Ronald L
RL
Dalman
Department of Radiation Oncology, University of Pennsylvania, Philadelphia, Pa.
Kevin T
KT
Nead
Department of Cardiovascular Sciences, Houston Methodist Research Institute, Houston, Tex; Center for Cardiovascular Regeneration, Houston Methodist DeBakey Heart and Vascular Center, Houston, Tex.
John P
JP
Cooke
Division of Vascular Surgery, Stanford Health Care, Stanford, Calif. Electronic address: nleeper@stanford.edu.
Nicholas J
NJ
Leeper
D000368:Aged; D000465:Algorithms; D055109:Ankle Brachial Index; D019540:Area Under Curve; D017023:Coronary Angiography; D057225:Data Mining; D016208:Databases, Factual; D003661:Decision Support Techniques; D005260:Female; D023281:Genomics; D006801:Humans; D016014:Linear Models; D016015:Logistic Models; D000069550:Machine Learning; D008297:Male; D008875:Middle Aged; D058729:Peripheral Arterial Disease / Q000145:classification / Q000175:diagnosis* / Q000235:genetics / Q000401:mortality; D011237:Predictive Value of Tests; D011379:Prognosis; D012372:ROC Curve; D015203:Reproducibility of Results; D018570:Risk Assessment; D012307:Risk Factors