Klasifikasi Status Batu Empedu Menggunakan KNN: Analisis Prediktif Berbasis Data Komposisi Tubuh dan Konfirmasi Diagnostik Visual (RapidMiner)
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Abstract
Gallstones are one of the disorders of the digestive system that are often difficult to detect in the early stages due to mild or non-specific symptoms. Delayed diagnosis can increase the risk of serious complications; therefore, a decision support method is needed to assist in classifying gallstone status quickly and accurately. This study aims to classify gallstone status using a data mining approach with the K-Nearest Neighbor (KNN) algorithm based on body composition data and visual diagnostic confirmation. The dataset used in this study consists of 319 patient records with 38 attributes, including age, gender, comorbid conditions, and other clinical indicators, with class labels representing gallstone status (true and false). The research process was conducted using RapidMiner software through several stages, including dataset retrieval, label determination, numerical-to-binominal data transformation, application of the KNN algorithm, and model performance evaluation using a confusion matrix and accuracy metrics. The experimental results show that the KNN algorithm achieved an accuracy of 52.98%. Although the accuracy obtained is considered moderate, the results indicate that the KNN algorithm can be applied as an initial method to support gallstone status classification. This study is expected to serve as a foundation for further development of medical prediction systems through improved data quality and algorithm parameter optimization
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