Open AccessOvarian Cancer Classification using Bayesian Logistic Regression
Author(s) -
Theresia Lidya Octaviani,
Zuherman Rustam,
Titin Siswantining
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/546/5/052049
Subject(s) - ovarian cancer , logistic regression , oncology , cancer , medicine , artificial intelligence , computer science
Cancer is one of the most common cause of death. One of the diseases that can be threaten women all over the world is ovarian cancer. Ovarian cancer is the eighth type of cancer that most women suffer from. Estimated that around 225.000 new cases are detected every year and around 140.000 people die each year from ovarian cancer. Based on WHO data, published in 2014, in Indonesia 7,6% of all cancer deaths are caused by ovarian cancer. So far there is no effective screening method for ovarian cancer. Current screening applications for high-risk women are still very controversial. There are many classification techniques has been applied for ovarian cancer prediction, for example deep learning, neuro fuzzy, neural network, and so many more. In this paper, we propose Bayesian logistic regression for ovarian cancer classification. We use data of patients suffer from ovarian cancer from RS Al-Islam Bandung to demonstrate the method. The accuracy expectation in this paper around 70%.