Analisis Komparatif Algoritma Klasifikasi Machine Learning untuk Memprediksi Diabetes
Abstract
Diabetes Mellitus is one of the most common chronic diseases, this disease is also a major concern in global public health issues. in this study, a Machine Learning approach was carried out to help predict diabetes in the community. Machine learning is very useful in analyzing health data because of its good ability to process large amounts of data. A comparative study with several machine learning classification algorithms such as K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Naive Bayes and Decision Tree (C4.5) has been conducted to determine which algorithm gives the best results in terms of predicting diabetes. Where, the features used in predicting diabetes include gender, age, history of hypertension, history of heart disease, history of smoking, BMI, level of HbA1c and blood glucose levels. From the results of this study, the accuracy rate of diabetes prediction for the K-Nearest Neighbors (KNN) algorithm is 92.5%, the Support Vector Machine (SVM) algorithm is 94.5%, the Naive Bayes algorithm is 90% and the last for the Decision Tree (C4.5) algorithm is 93.5%. So, from the test results of several machine learning classification algorithms it can be concluded that the Support Vector Machine (SVM) algorithm is the most optimal algorithm in terms of predicting diabetes.
Keywords: Machine Learning, KNN, SVM, Naive Bayes, Decision Tree, Diabetes.
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DOI: http://dx.doi.org/10.30829/algoritma.v9i1.23794
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