Improving Naïve Bayes performance in single image pap smear using weighted principal component analysis (WPCA)

The accuracy value for single image classification of Pap smear for seven classes still has an unsatisfactory accuracy whereas the determination of cell classes in single image of Pap smear is very important to determine whether the cells are normal or not. Pap smear image classification that produces good accuracy will greatly assist in the process of detecting cells automatically. This study aims to determine whether the use of the method of weighted — Principal Component Analysis (PCA) models can improve the performance of the Naïve Bayes algorithm to classify cell images in the Herlev dataset. Accuracy will be checked for the classification of two classes and seven classes. The method used in this research consists of several stages that are preprocessing, knowledge rule, evaluation, and performance report. The results of this study indicate that the weighted PCA method can improve the accuracy on the classification of seven classes while the classification of two classes does not provide better accuracy results.