Comparison of Logistic Regression, Random Forest and Adaboost Algorithms for Diabetes Mellitus Classification
DOI:
https://doi.org/10.57152/ijatis.v1i1.1116Keywords:
Adaboost, Classification, Diabetes Mellitus, Logistic Regression, Random ForestAbstract
Diabetes mellitus is a chronic disease that affects the way the body regulates sugar (glucose). High blood sugar levels can lead to health complications including heart problems, eye disorders, nerve damage, kidney and blood vessel disorders. It is important for early detection of diabetes by utilizing data mining technology. Data mining has various classification models that can be used to detect diabetes, including logistic regression, random forest and adaboost. The comparison of the three algorithms aims to find out which algorithm is most appropriate in the classification of diabetes. From the results obtained, the random forest algorithm has the best performance in the classification of diabetes mellitus compared to other algorithms.
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