<![CDATA[Diabetes is a prevalent disease in humans that is caused by excessive sugar levels in the body. If left untreated, it can lead to severe consequences such as paralysis, decay in certain parts of the body, and even death. Unfortunately, early detection of diabetes is difficult, and many cases go untreated until it is too late. However, the development of technology has opened up new possibilities for early detection and treatment of diabetes. One such approach is classification, a commonly used method in the field of Computer Science. Classification is used in various fields, including health, agriculture, and animal diseases, to draw conclusions based on input data using cause-and-effect relationships. Many different learning concepts and methods can be used in classification, with the Decision Tree concept being one of the most popular examples. This study compares several classification methods, including Decision Tree, Random Forest, AdaBoost, and Stochastic Gradient Boost, with feature selections carried out using MI and IF. The study aims to evaluate the effectiveness of these methods and the influence of feature selection on improving their performance. Based on the results of the study, it can be concluded that feature selection using Mutual Information and Importance Feature can improve the classification accuracy in some methods, particularly in Random Forest, AdaBoost, and Stochastic Gradient Boost. However, the Decision Tree algorithm did not show any improvement in accuracy after feature selection. The best classification accuracy was achieved with the Stochastic Gradient Boost method using the original dataset without feature selection, while the Random Forest method showed the highest accuracy after using all the features. Overall, the results suggest that feature selection can be a useful technique for improving the performance of classification algorithms in diabetes prediction. The study suggests that future research could investigate other classification methods, such as Neural Network or Deep Learning, and use optimization algorithms like Genetic Algorithm or Particle Swarm Optimization to improve feature selection results.]]>
