The Influence of Determining the K-Value on Improving the Diabetes Classification Model using the K-NN Algorithm

Abstract

Diabetes mellitus is still an important health problem globally, so it requires an efficient classification model to help determine a patient's diagnosis. This study aims to determine the K-value on the accuracy performance of the diabetes classification model using the K-Nearest Neighbors (K-NN) algorithm. This research utilizes a simulated dataset generated through interaction with ChatGPT, we investigate various K-values ​​in the K-NN model and assess its accuracy using a confusion matrix. Based on experiments, we found that the K-NN classification model with a K=6 obtained an optimal accuracy of 97.62%. Thus, our findings highlight the important role of selecting optimal K-values ​​in improving the performance of diabetes classification models.