Hepatitis Cluster Model With K-Means Algorithm

• Published in: 2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE) pp. 811 - 815
• Main Authors: Chusyairi, Ahmad, Nurdiawan, Odi, Sambath, Khoem, Hayat, Rachmad Nur, Arie Wijaya, Yudhistira
• Format: Conference Proceeding
• Language: English
• Published: IEEE 16.02.2023
• Subjects: Clustering; Clustering algorithms; Computer viruses; Data models; Feature extraction; Hepatitis; K-Means; Liver diseases; Medical services; Merging; Ministry of Health
• DOI: 10.1109/ICCoSITE57641.2023.10127719

Indonesia is currently being hit by cases of mysterious hepatitis, the Indonesian Ministry of Health announced on May 18, 2022, that there have been 14 cases of hepatitis, consisting of 1 probable case and 13 pending classification cases, with information on 1 probable case from West Sumatra, 7 cases in Jakarta, 1 case in Jambi, and 3 cases in East Java. At the time when this paper was written, Jaundice that age under 10, and the absence of any other etiology are common symptoms of strange hepatitis. Fever, diarrhea, stronger urine, and pale stools are symptoms that surface. The use of intelligent techniques appears to be necessary to interpret the complicated data set's structure and effectively support doctors during the diagnosis process. As a result, this research effort provides an intelligent hybrid technique for the diagnosis of hepatitis disease by merging enhanced k-means clustering and ensemble learning algorithms. The benefit of employing ensemble learning is that it creates a set of hypotheses by utilizing numerous students to address a particular issue. The data in this study uses a primary data model, which means that the data is taken directly from the source. In this study, the data is obtained from kaggle.com, which is then performed by feature selection leaving several features, namely age, bilirubin, alk_phosphate, sgot, albumin, and protime which are the important feature to be used on K-Means clustering.