Prostate Cancer Classification Using Random Forest and Support Vector Machines

Z. Rustam, N. Angie

Research output: Contribution to journalConference articlepeer-review

4 Citations (Scopus)


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.

Original languageEnglish
Article number012043
JournalJournal of Physics: Conference Series
Issue number1
Publication statusPublished - 15 Feb 2021
Event3rd International Conference on Statistics, Mathematics, Teaching, and Research 2019, ICSMTR 2019 - Makassar, Indonesia
Duration: 9 Oct 201910 Oct 2019


  • classification
  • Prostate cancer
  • random forest
  • support vector machines


Dive into the research topics of 'Prostate Cancer Classification Using Random Forest and Support Vector Machines'. Together they form a unique fingerprint.

Cite this