Open AccessPossibilistics C-Means (PCM) Algorithm for the Hepatocellular Carcinoma (HCC) Classification
Author(s) -
Rafiqatul Khairi,
Zuherman Rustam,
Suarsih Utama
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/546/5/052038
Subject(s) - hepatocellular carcinoma , computer science , algorithm , artificial intelligence , medicine
Hepatocellular Carcinoma (HCC) is a malignant tumor that attacks the liver and can cause death. Although there have been advances in technology for the prevention, diagnosis, and treatment, the number of liver cancer patients is still increasing. The liver can still function normally even if some of its parts are not in good condition. Therefore, the symptoms of liver cancer at an early stage are difficult to detect. Early diagnosis of this disease will increase the chances of recovery. One method to diagnose Hepatocellular Carcinoma (HCC) is to check the level of alpha-fetoprotein (AFP) in the blood which is alpha-fetoprotein (AFP) is a cancer index. If the liver cancer cells continue to grow, the level of alpha-fetoprotein (AFP) will be very high. This paper presents a Possibilistic C-Means (PCM) algorithm, which used to classify the results of alpha-fetoprotein (AFP) blood tests to determine whether patients diagnosed with Hepatocellular Carcinoma (HCC) or normal patients. This method will help to get an accuracy of about 92%.