Abstract:

Cervical cancer is the fourth most common cancer in women and also causes death. In this study, it was obtained from the ‘UC Irvine Machine Learning Repository’ database, which has 32 risk factors, 4 target variables and 858 records to diagnose cervical cancer by machine learning methods. The names of four target variables in the data set are Schiller, Citology, Biopsy, and Hinselmann. In this study, the Support Vector Machine (SVM) and kNN algorithms were applied for each target variable and the correct classification performance and time performance for each target were compared. The Gaussian kernel function for SVM and the Euclidean distance measure for kNN were used. As a result of experimental studies it was observed that SVM was more successful than kNN.

Keywords: