Open AccessComparison of risk models for mortality and cardiovascular events between machine learning and conventional logistic regression analysis
Author(s) -
Shinya Suzuki,
Takeshi Yamashita,
Tsuyoshi Sakama,
Takuto Arita,
Nobuyuki Yagi,
Takayuki Otsuka,
Hiroaki Semba,
Hiroto Kuwabara,
Seiki Matsuno,
Yuko Kato,
Tokuhisa Uejima,
Yuji Oikawa,
Minoru Matsuhama,
Junji Yajima
Publication year - 2019
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0221911
Subject(s) - logistic regression , regression analysis , regression , medicine , computer science , artificial intelligence , statistics , machine learning , mathematics
Aims Non-linear models by machine learning may identify different risk factors with different weighting in comparison to conventional linear models. Methods and results The analyses were performed in 15,933 patients included in the Shinken Database (SD) 2004–2014 ( n = 22,022) for whom baseline data of blood sampling and ultrasound cardiogram and follow-up data at 2 years were available. Using non-linear models with machine learning software, 118 risk factors and their weighting of risk for all-cause mortality, heart failure (HF), acute coronary syndrome (ACS), ischemic stroke (IS), and intracranial hemorrhage (ICH) were identified, where the top two risk factors were albumin/hemoglobin, left ventricular ejection fraction/history of HF, history of ACS/anti-platelet use, history of IS/deceleration time, and history of ICH/warfarin use. The areas under the curve of the developed models for each event were 0.900, 0.912, 0.879, 0.758, and 0.753, respectively. Conclusion Here, we described our experience with the development of models for predicting cardiovascular prognosis by machine learning. Machine learning could identify risk predicting models with good predictive capability and good discrimination of the risk impact.