Performance Evaluation Using Classifier Algorithm On Endometrial Cancer Data - Juliet, A. Hency; Padmajavalli, R (International Journal of Computer Sciences and Engineering, 2025)
Many factors affecting the success of data mining techniques, the pureness of data are one of the factors. The inclusion of irrelevant and noisy data in the pattern analyzing phase, can results poor predicting performance. To discover information from the endometrial carcinoma data set, the pre-processing technique such as cleaning, transforming and modelling are applied. Diverse kinds of pre-processing techniques were functions in the data set in order to work with the full pledged data set. Methodology Used: The data mining tool WEKA is used for feature selection. Using various classifiers, evaluators and search methods six features are selected out of eighteen, attributes. The performance evaluation was done using RStudio. The accuracy of the classifiers model Random Forest and Naïve Bayes are checked for the minimized and full data sets. The hybrid model was formed by combing both the models to improve the performance of the classifier model. Findings: The Hybrid model was adopted for the performance evaluation by combining naïve Bayes and random forest classifier and the accuracy of the new model is 93.55%.