Machine learning algorithms to predict major adverse cardiovascular events in patients with diabetes.
Comput Biol Med
; 164: 107289, 2023 09.
Article
en En
| MEDLINE
| ID: mdl-37557056
ABSTRACT
BACKGROUND:
Major Adverse Cardiovascular Events (MACE) are common complications of type 2 diabetes mellitus (T2DM) that include myocardial infarction (MI), stroke, and heart failure (HF). The objective of the current study was to predict MACE among T2DM patients.METHODS:
Type 2 diabetes mellitus patients above 18 years old were recruited for the study from the All of Us Research Program. Eligible participants were those who took sodium-glucose cotransporter 2 inhibitors. Different Machine learning algorithms including RandomForest (RF), XGBoost, logistic regression (LR), and weighted ensemble model (WEM) were employed. Clinical attributes, electrolytes and biomarkers were explored in predicting MACE. The feature importance was determined using mean decrease accuracy.RESULTS:
Overall, 9, 059 subjects were included in the analyses, of which 5197 (57.4%) were females. The XGBoost Model demonstrated a prediction accuracy of 0.80 [0.78-0.82], which is higher as compared to the RF 0.78[0.76-0.80], the LR model 0.65 [0.62-0.67], and the WEM 0.75 [0.73-0.76], respectively. The classification accuracy of the models for stroke was more than 95%, which was higher than prediction accuracy for MI (â¼85%), and HF (â¼80%). Phosphate, blood urea nitrogen and troponin levels were the major predictors of MACE.CONCLUSION:
The ML models had shown acceptable performance in predicting MACE in T2DM patients, except the LR model. Phosphate, blood urea nitrogen, and other electrolytes were important predictors of MACE, which is consistent between the individual components of MACE, such as stroke, MI, and HF. These parameters can be calibrated as prognostic parameters of MACE events in T2DM patients.Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Enfermedades Cardiovasculares
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Accidente Cerebrovascular
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Diabetes Mellitus Tipo 2
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Salud Poblacional
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Insuficiencia Cardíaca
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Infarto del Miocardio
Tipo de estudio:
Etiology_studies
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Prognostic_studies
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Risk_factors_studies
Límite:
Adolescent
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Female
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Humans
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Male
Idioma:
En
Revista:
Comput Biol Med
Año:
2023
Tipo del documento:
Article
País de afiliación:
Estados Unidos