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This study aims to develop models to accurately classify patients with type 2 diabetes using the Practice Fusion dataset. We use Random Forest (RF), Support Vector Classifier (SVC), AdaBoost classifier,… Click to show full abstract
This study aims to develop models to accurately classify patients with type 2 diabetes using the Practice Fusion dataset. We use Random Forest (RF), Support Vector Classifier (SVC), AdaBoost classifier, an ensemble model, and automated machine learning (AutoML) model. We compare the performance of all models in a five-fold cross-validation scheme using four evaluation measures. Experimental results demonstrate that the AutoML model outperformed individual and ensemble models in all evaluation measures.
Keywords:
machine learning;
automated machine;
customized automated;
learning based;
based models
Journal Title: Studies in health technology and informatics
Year Published: 2022
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Journal Title: Studies in health technology and informatics
Year Published: 2022
Link to full text (if available)
Share on Social Media: Sign Up to like & get
recommendations!