Feature Selection using Random Forest Classifier for Predicting Prostate Cancer

Abstract

Prostate cancer is cancer that attacks the prostate gland, usually affecting men over 50 years. Prostate cancer is a disease that develops slowly. Based on this, rapid and precise detection is needed so that the disease can be treated immediately. This study focuses on the application Feature Selection using the Random Forest Classifier to detect prostate cancer. The Random Forest Classifier is a method of classifying data by determining the decision tree. The use of more trees will affect the accuracy to be obtained for the better. The Random Forest Classifier can classify data that has incomplete attributes and can be used to handle large sample data. Selection of features is an important process because it can affect the accuracy of classification. This method increases accuracy by about 87%. Thus, the selection of features can improve accuracy in the detection of prostate cancer.

Pancreatic cancer classification using logistic regression and random forest

“…Random forest is a method developed by Breiman in 2001 [16], [17]. Random forest works when it reaches maximum accuracy, a decision tree can be used to avoid overfitting data [18]. The estimation process previously carried out by decision tree and CART was enhanced by Breiman, which was started by randomly selecting m variables from several independent variables.…”

Acute sinusitis data classification using grey wolf optimization-based support vector machine

“…The previous research on support vector machine was proposed to process this data [4]. Also, many researches before using other methods of classification for classifying disease data, such as binary logistic regression for ovarian cancer classification [5], fast fuzzy clustering for breast cancer data [6], naive bayes classifier for predicting colon cancer [7], random forest classifier for predicting prostate cancer [8], and many more. Therefore, in this research, grey wolf optimization-support vector machine (GWO-SVM) will be the machine learning technique used, where the GWO technique will be used to tuned the parameters in SVM.…”

Pancreatic Cancer Early Detection Using Twin Support Vector Machine Based on Kernel

“…Machine learning has been accepted for various classification problems in various fields, one of which is in the field of medicine. In the field of medicine, several machine learning methods are used to detect several types of cancer, namely breast cancer [8][9][10][11], cervical cancer [12,13], ovarian cancer [14,15], colon cancer [16], prostate cancer [17], and lung cancer [18].…”