Utilizing KNN for Classification an Exploration with the Diabetes Dataset

This study explores the application of the K-Nearest Neighbors (KNN) algorithm for classification using the widely utilized Diabetes dataset. The dataset contains various biomedical attributes such as glucose level, blood pressure, and body mass index, along with a target variable indicating diabetes onset within a five-year period. The objective is to predict the onset of diabetes based on these attributes.The KNN algorithm, a non-parametric and instance-based method, is employed for classification. By measuring the proximity of a data point to its k nearest neighbors in the feature space, the algorithm assigns a class label to each data point. Cross-validation techniques are utilized to determine the optimal value of k, ensuring robust performance of the model. The results reveal the effectiveness of the KNN algorithm in accurately classifying diabetic and non-diabetic individuals based on their biomedical attributes. Performance metrics such as accuracy, precision, recall, and F1-score are employed to evaluate the model's predictive performance. This study contributes to the understanding of the KNN algorithm's utility in healthcare applications, particularly in the domain of disease prediction and classification using biomedical data. The findings underscore the potential of KNN as a valuable tool for assisting healthcare practitioners in early diagnosis and intervention strategies for diabetes management.

KEYWORDS: K-Nearest Neighbors (KNN) algorithm