Literature Review: Penggunaan Machine Learning Berbasis SVM untuk Klasifikasi Penyakit Liver

Amir Rozy Sinaga; Raihan Rizky Nurfadila; Melanie Putria; Silvi Maerosa

Authors

Amir Rozy Sinaga Universitas Pamulang
Raihan Rizky Nurfadila Universitas Pamulang
Melanie Putria Universitas Pamulang
Silvi Maerosa Universitas Pamulang

Keywords:

Liver Disease Classification System, SVM Algorithm, Literature Review, Machine Learning

Abstract

Liver disease is a significant health problem worldwide, with high mortality rates if not diagnosed early. In this study, we conducted a literature review on the application of Support Vector Machine (SVM) in liver disease classification. The SVM method was chosen because of its ability to handle imbalanced data and high complexity. The purpose of this study was to evaluate the effectiveness of SVM compared to other machine learning methods in detecting liver disease. The results of the literature review showed that SVM provided higher accuracy compared to other methods such as Naïve Bayes and Decision Tree, with some studies achieving accuracy of more than 90%. This study is expected to provide insight into the development of a machine learning-based diagnostic system for liver disease.

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