Comparative study on data mining classification methods for cervical cancer prediction using pap smear results

Abstract

The number of woman with cervical cancer in Indonesia is getting higher. Indonesia becomes the country with the highest number of women with cervical cancer in the world. Cervical cancer became the highest cause of cancer deaths in women globally. There has been a lot of research using data mining techniques with variety of different data mining models that can be used for analyzing cervical cancer. In this research, data that be used were obtained from the medical records of the Pap smear test results. There are 38 symptoms and 7 classes. Naïve Bayes, Support Vector Machines (SVM), and Random Forest Tree was used to evaluate the performance of the classifier. The performance matric that used in this study are accuracy, recall, precision, and ROC curve. Based on the performance matric, Random Forest Tree is the best classifier among other classifiers to classify Pap smear results.