Comparison of Logistic Regression, Random Forest and Adaboost Algorithms for Diabetes Mellitus Classification

Alfi Syahri (Universitas Islam Negeri Sultan Syarif Kasim Riau, Indonesia)
Umi Fariha (Universitas Islam Negeri Sultan Syarif Kasim Riau, Indonesia)
Rival Afandi (Universitas Islam Negeri Sultan Syarif Kasim Riau, Indonesia)
Intan Nurliyana (MARA University, Malaysia)

Indonesian Journal of Applied Technology and Innovation Science
Vol. 1 No. 1 (2024): IJATIS February 2024

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.