Open AccessProstate Cancer Classification Using Random Forest and Support Vector Machines
Author(s) -
Zuherman Rustam,
N. Angie
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1752/1/012043
Subject(s) - prostate cancer , random forest , support vector machine , cancer , ethnic group , medicine , artificial intelligence , computer science , machine learning , political science , law
Nowadays, it gets more types of diseases in the medical sector. For this reason, the role of technology is very important in assisting medical staff to overcome the problem. This research discusses about Prostate Cancer. Prostate Cancer is suffered commonly by males. There are no exact causes how Prostate Cancer occurs in males, but there are several risk factors of a Prostate Cancer, such as age, ethnic group, family history, diet, smoking, and world area. In this research, the classification to diagnose Prostate Cancer is using two methods, those are Random Forest (RF) and Support Vector Machines (SVM). By comparing accuracy of those two methods, we will know which method is better with a dataset that we have from Al-Islam Bandung Hospital, Indonesia. The result is given that Random Forest has a better accuracy than Support Vector Machines. The accuracy shows 97.30% with 80% of data training.