Comparison of risk models for mortality and cardiovascular events between machine learning and conventional logistic regression analysis


Autoři: Shinya Suzuki aff001;  Takeshi Yamashita aff001;  Tsuyoshi Sakama aff002;  Takuto Arita aff001;  Naoharu Yagi aff001;  Takayuki Otsuka aff001;  Hiroaki Semba aff001;  Hiroto Kano aff001;  Shunsuke Matsuno aff001;  Yuko Kato aff001;  Tokuhisa Uejima aff001;  Yuji Oikawa aff001;  Minoru Matsuhama aff003;  Junji Yajima aff001
Působiště autorů: Department of Cardiovascular Medicine, The Cardiovascular Institute, Tokyo, Japan aff001;  Sigmaxyz, Inc, Tokyo, Japan aff002;  Department of Cardiovascular Surgery, The Cardiovascular Institute, Tokyo, Japan aff003
Vyšlo v časopise: PLoS ONE 14(9)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0221911

Souhrn

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.

Klíčová slova:

Medicine and health sciences – Cardiology – Heart failure – Ejection fraction – Diagnostic medicine – Signs and symptoms – Pathology and laboratory medicine – Hemorrhage – Vascular medicine – Blood pressure – Neurology – Cerebrovascular diseases – stroke – Ischemic stroke – Pharmaceutics – Drug therapy – Antiplatelet therapy – Research and analysis methods – Mathematical and statistical techniques – Statistical methods – Physical sciences – Mathematics – Statistics – Computer and information sciences – Artificial intelligence – Machine learning


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Článek vyšel v časopise

PLOS One


2019 Číslo 9
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