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The results reveal that ensemble learning algorithms based on boosting, particularly AdaBoost and XGBoost, outperform other supervised machine learning methods.
Machine learning in precision diabetes care and cardiovascular ...
www.ncbi.nlm.nih.gov › PMC10521578
Sep 25, 2023 · Supervised learning can be used to better predict future cardiovascular risk (regression), or the presence of diabetic retinopathy ( ...
Our study used the Cardiovascular Disease dataset and conducts experiments with various supervised machine learning algorithms, such as Naive Bayes, decision ...
Jan 19, 2023 · Machine learning (ML) approach offers the opportunity to identify patients at greater risk of T2DM complications [14] while prediction models ...
Supervised Machine Learning Approach for Predicting. Cardiovascular Complications Risk in Patients with Diabetes Mellitus. TechRxiv. Preprint. https://doi ...
Health screening plays a pivotal role in stratifying the risk levels of diabetes patients, facilitating proactive measures to prevent the progression of ...
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Nov 6, 2019 · In this paper, we use supervised machine learning models to predict diabetes and cardiovascular disease. Despite the known association ...
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Aug 24, 2023 · We aimed to find important factors that predict cardiovascular (CV) risk using a machine learning (ML) approach.
The results reveal that ensemble learning algorithms based on boosting, particularly AdaBoost and XGBoost, outperform other supervised machine learning ...
A supervised machine learning approach that incorporates genetic algorithms (GA) and weighted k-nearest neighbours (WkNN) was applied to classify type 2 ...
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