Key result
A Hybrid Prediction Model integrating DBSCAN, SMOTE, and Random Forest provided the best performance compared to other models for predicting type 2 diabetes and hypertension.
Why the study?
Does a Hybrid Prediction Model using DBSCAN, SMOTE, and Random Forest improve the early prediction of type 2 diabetes and hypertension compared to other models?
Population
Three benchmark datasets containing risk factors for type 2 diabetes and hypertension
Comparison
Hybrid Prediction Model integrating DBSCAN-based… vs Other prediction models
Authors
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May support IoT-based early prediction of diabetes and hypertension; leaves open prospective clinical validation.
Does a Hybrid Prediction Model using DBSCAN, SMOTE, and Random Forest improve the early prediction of type 2 diabetes and hypertension compared to other models?
A hybrid machine learning model combining DBSCAN, SMOTE, and Random Forest improves the early prediction of type 2 diabetes and hypertension and can be integrated into IoT-based healthcare monitoring systems.
Ijaz et al. (2018) studied Type 2 diabetes and hypertension. Hybrid Prediction Model (HPM) vs. Other models was evaluated on Prediction of type 2 diabetes and hypertension. A Hybrid Prediction Model integrating DBSCAN, SMOTE, and Random Forest provided the best performance compared to other models for predicting type 2 diabetes and hypertension.
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