A Comparison of Five Machine Learning Algorithms in the Classification of Diabetes Dataset
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Abstract
Diabetes is a disease that has no permanent cure; hence early detection is required with high accuracy. This study
aims to compare five machine learning (ML)algorithms and achieve the best accuracy for predicting early stage diabetes. The
dataset from the hospital Frankfurt, Germany includes information on 2000 patients as well as nine distinct character istics for
each of them is used in this work. Five ML Algorithms used for datasets to predict diabetes are Random Forest (RF), KNearest
Neighbor (KNN), Gaussian Naïve Bayes (NB), support vector machine (SVM), and Logistic Regression (LR).
However, according to the obtained results, it is observed that the proposed model with RF has achieved an excellent result of
accuracy value = 99% during the comparison with a rest classification algorithm that is used in the proposed model. In
addition, the proposed model's efficiency has been compared to previous work, and it has achieved the highest accuracy.
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