APPLICATION OF THE K-NEAREST NEIGHBOR ALGORITHM FOR EARLY DETECTION OF NUTRITIONAL STATUS IN ADULT PATIENTS
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Abstract
Assessing the nutritional status of adult patients is essential to gain a comprehensive understanding of their condition and assist healthcare workers in planning appropriate treatment. However, manual assessment is time-consuming and labor-intensive, especially when the number of patients exceeds the number of available healthcare workers. This can hinder the timely and accurate delivery of nutritional care. The K-Nearest Neighbor (KNN) algorithm is a commonly used method for nutritional status classification, particularly in toddlers, pregnant women, or for obesity classification in adults. The use of KNN for early detection of adult nutritional status remains rarely explored. This study applies the KNN algorithm to classify the nutritional status of adult patients using data from the Alamanda 1 ward and the ICU ward at Sleman Regional General Hospital, collected from January 2 to October 18, 2023. The dataset includes patient height, weight, and nutritional status. The algorithm was implemented using RapidMiner with odd k-values less than 20, and data splits of 90:10, 70:30, and 50:50 for training and testing. Results show that the optimal k-values for the highest accuracy were k = 1 and k = 3 using the 70:30 data split, both achieving an accuracy of 96.77%. The highest sensitivity, 97.61%, was also achieved at k = 3 with the same data split. The KNN algorithm demonstrates strong potential to be developed into an early detection system for assessing the nutritional status of adult patients in hospitals, supporting faster and more accurate nutritional care services
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