Key result
Modified ID3 decision tree model predicts diabetes complications with ~92% accuracy.
Why the study?
The study was conducted to develop a prediction model for diabetes complications, including eye disease, kidney disease, coronary heart disease, and hyperlipidemia.
Can a modified ID3 decision tree model accurately predict complications of diabetes?
Can a modified ID3 decision tree model accurately predict complications of diabetes?
A modified ID3 decision tree model demonstrated high accuracy in predicting diabetes complications, suggesting potential utility as a mobile health tool.
Supports mobile prediction tools for diabetes complications; hypothesis-generating pending prospective validation.
The objective of this work is to develop the prediction model of complications of diabetes including eye disease, kidney disease, coronary heart disease and hyperlipidemia. The main conceptual framework is the modification of the forecasting model created from the Iterative Dichotomiser 3 (ID3) decision tree into a binary number vector format. The model efficiency was tested by the 10-fold cross-validation method. The results show that the model gives the accuracy of 92.35 percent. Mobile application was constructed to test the model with general patients. The prediction results give 100 percent of accuracy.
No takes yet. Share an insight, caveat, or question.
Kantawong et al. (2020) studied Diabetes complications. Modified Iterative Dichotomiser 3 (ID3) decision tree prediction model was evaluated on Model accuracy. A prediction model for diabetes complications based on a modified ID3 decision tree achieved 92.35% accuracy in cross-validation and 100% accuracy in a mobile application test.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: