TY - JOUR
T1 - Prostate Cancer Classification Using Random Forest and Support Vector Machines
AU - Rustam, Z.
AU - Angie, N.
N1 - Funding Information:
This research supported financially by the Ministry of Research, Technology, and Higher Education Republic of Indonesia (KEMENRISTEKDIKTI) with PTUPT 2020 research grant scheme.
Publisher Copyright:
© Published under licence by IOP Publishing Ltd.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021/2/15
Y1 - 2021/2/15
N2 - 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.
AB - 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.
KW - classification
KW - Prostate cancer
KW - random forest
KW - support vector machines
UR - http://www.scopus.com/inward/record.url?scp=85101733676&partnerID=8YFLogxK
U2 - 10.1088/1742-6596/1752/1/012043
DO - 10.1088/1742-6596/1752/1/012043
M3 - Conference article
AN - SCOPUS:85101733676
SN - 1742-6588
VL - 1752
JO - Journal of Physics: Conference Series
JF - Journal of Physics: Conference Series
IS - 1
M1 - 012043
T2 - 3rd International Conference on Statistics, Mathematics, Teaching, and Research 2019, ICSMTR 2019
Y2 - 9 October 2019 through 10 October 2019
ER -
