Analysis of diabetes disease using k-nearest neighbor (KNN) and naive bayes methods
Keywords:
classification, Diabetes Mellitus, K-Nearest Neighbor, Machine Learning, Naïve BayesAbstract
Diabetes mellitus is a chronic disease whose prevalence continues to increase and has the potential to cause serious complications if not detected early. Therefore, a classification method is needed capable of assisting the diagnosis process quickly and accurately. This study aims to analyze and compare the performance of the K-Nearest Neighbor (KNN) and Naïve Bayes algorithms in the classification of diabetes. The dataset used was obtained from Kaggle, totaling 768 data points with 8 attributes: Pregnancies, Glucose, BloodPressure, SkinThickness, Insulin, BMI, DiabetesPedigreeFunction, Age, and the Outcome label. The dataset is divided into 80% training data and 20% testing data. Model evaluation was carried out using accuracy, precision, recall, and F1-score metrics. The results showed that the KNN method produced the best accuracy of 75% at a value of k = 3, while the Naïve Bayes method produced an accuracy of 77.92%. Based on these evaluation results, the Naïve Bayes method has better performance compared to KNN in classifying diabetes in the dataset used. This research is expected to be a reference in the development of clinical decision support systems for the early diagnosis of diabetes.
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