Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Predicting cardiovascular disease among diabetic patients in Ethiopia using machine learning models: evidence from Ethiopian public health Institute data (2024/2025)
1
Zitationen
4
Autoren
2025
Jahr
Abstract
The gradient boosting machine (GBM) model demonstrated the highest performance compared to the other models, achieving an accuracy of 93%. The most significant factors influencing the GBM model were total cholesterol, hypertension, and fasting blood glucose levels. The gradient boosting model shows potential for future integration into clinical decision-support systems, pending external validation and early prediction of cardiovascular disease in individuals with diabetes.
Ähnliche Arbeiten
Biostatistical Analysis
1996 · 35.446 Zit.
UCI Machine Learning Repository
2007 · 24.290 Zit.
An introduction to ROC analysis
2005 · 20.698 Zit.
The use of the area under the ROC curve in the evaluation of machine learning algorithms
1997 · 7.122 Zit.
A method of comparing the areas under receiver operating characteristic curves derived from the same cases.
1983 · 7.066 Zit.