Prediction of Liver Disease Using a Linear Regression Algorithm

Deny Haryadi; Dewi Marini Umi Atmaja; Arif Rahman Hakim

doi:10.52661/j_ict.v5i1.182

Authors

Deny Haryadi Universitas Telkom
Dewi Marini Umi Atmaja Universitas Medika Suherman
Arif Rahman Hakim Universitas Medika Suherman

DOI:

https://doi.org/10.52661/j_ict.v5i1.182

Keywords:

Liver Disease, Prediction, Data Mining, Linear Regression Algorithm

Abstract

The liver is the most essential organ in the human body. Hepatitis is one such disorder affecting the liver and is a global health issue, including in Indonesia. Liver disease is an inflammatory condition of the liver that can be triggered by genetic factors, viral infections, alcohol consumption, and the use of certain drugs. In principle, prevention of hepatitis or liver disease can be done by adopting a healthy lifestyle. In addition, early detection is also very important in preventing death in those affected by this disease. One method for early detection is through the application of data mining, which can help predict and reduce mortality in patients affected by this disease. Linear regression is a data mining technique utilized to predict the dependent variable or outcome based on the independent variable or predictor. The study conducted tests on this algorithm and obtained a Root Mean Squared Error of 0.349 +/- 0.000. This indicates the presence of a correlation or functional relationship (cause and effect) between the dependent variable (criterion) and the independent variable (predictor). The purpose of this testing process is to detect liver disease using the linear regression algorithm.

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