Abstract
To improve the accuracy in detection of diabetes type diagnosis using Random Forest Algorithm and Support Vector Method. A dataset on diabetes was collected on kaggle.com. Support vector method analysis was performed on the data using kernel as linear, poly and rbf with random state = 42 and test size = 0.2. The Random Forest Algorithm with n_estimators = 100 has same accuracy with support vector method with kernel as rbf. When it comes to type diagnosis for patient having diabetes, the Random Forest Algorithm and Support Vector Method with kernel rbf can be used unlike other kernel of Support Vector Method.