Liver Disease Prediction using Decision Tree Algorithm

Selly

doi:10.63017/jdsi.v4i1.138

Authors

Selly institut bisnis dan teknologi pelita indonesia

DOI:

https://doi.org/10.63017/jdsi.v4i1.138

Keywords:

algorithm, dataset, diseases, liver, prediction

Abstract

The liver is one of the important organs in the human body that functions to detoxify or neutralize toxins from everything that enters our body, making the body healthier. The liver can be affected by diseases that can disrupt its function; when liver disease attacks, toxins will spread throughout the body, making it unhealthy. Liver disease is a condition caused by viruses, alcohol, lifestyle factors, and others. According to WHO (World Health Organization) data, nearly 1.2 million people die each year, particularly in Southeast Asia and Africa, due to liver disease. Individuals often do not realize or are late in detecting liver disease, so by the time they are examined, the disease is already severe. Early intervention would be better if symptoms are recognized. Data mining can assist in diagnosing liver disease more easily, especially in helping doctors determine whether a patient suffers from liver disease based on symptoms that closely resemble liver conditions. The diagnosis process for liver disease is carried out through classification, resulting in whether the patient has liver disease or not. This study uses five data mining algorithms: Naïve Bayes, K-Nearest Neighbor (KNN), Decision Tree, Random Forest, and Deep Learning.

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