Design and Development of Artificial Intelligence Based Applications Using Classification Algorithms in Datamining to Predict Early Diabetes
DOI:
https://doi.org/10.55123/ijisit.v1i1.2Keywords:
Diabetes Prediction Application, Prediction Naive Bayes, RAD Method, Classification ComparationAbstract
Diabetes is a chronic disease characterized by high blood sugar levels. High blood sugar levels can cause various serious complications, such as heart disease, stroke and kidney failure. Early detection of diabetes is essential to prevent this complication. This research is using experimental method. The experimental method is a research method carried out by deliberately manipulating the independent variable to see its effect on the dependent variable. In this case, the independent variable being manipulated is the machine learning algorithm used to predict diabetes. The dependent variable observed was the accuracy of the diabetes prediction model. From the research results, it was found that the Naive Bayes model had the highest accuracy, namely 81.5%. This model is better than the Decision Tree C.45 (72.7%), Random Forest (73.8%), and K-Nearest Neighbor (71.5%) models. Based on the research results, it can be concluded that the Naive Bayes model is the best model for predicting diabetes. The appropriate system development method for this research is the Rapid Application Development (RAD) method. Further research can be carried out to improve the accuracy of diabetes prediction models by using optimization models, such as Particle Swarm Optimization or Colony Optimization. In addition, further research can be carried out to develop diabetes prediction applications that can predict diseases that may be caused by diabetes, such as heart disease, stroke, kidney disease and blindness.
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