Implementasi Algoritma K-Nearest Neighbor (K-NN) dalam Deteksi Dini Penyakit Hepatitis C
Abstract
According to the World Health Organization (WHO), Hepatitis is an inflammatory condition that can evolve into Cirrhosis or liver cancer. Hepatitis is a disease that is caused by several types of viruses that attack and cause inflammation and damage to the cells of the human liver. Hepatitis C Virus (HCV) is one of the viruses that caused hepatitis and is considered the biggest impact among the other viruses that caused hepatitis. This study uses a classification method with the K-Nearest Neighbor (KNN) algorithm to detect the onset of hepatitis C in patients based on data from the patient’s laboratory checks. The classification method with K-Nearest Neighbor (KNN) algorithm is carried out by comparing the neighbors between test data and train data based on the patient’s medical history. The tuning parameter is used to determine the number of neighbors or the value of K in K-Nearest Neighbor (KNN) which obtains 92% of accuracy, 92% of precision, and 99% of recall with an 80:20 ratio of training data and test data.
References
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