Classification of benign and malignant tumor cells using bagging and Adaboost classifiers from Wisconsin dataset for the potential diagnostic application - Koyyala, Umakanth; Thirunavukkarasu, Usharani (AIP conference proceedings, 2024)
Aim: The aim of the study is to classify the benign and malignant tumor cells using Bagging and Adaboost classifiers from Wisconsin dataset for the potential diagnostic application. Materials and Methods: Wisconsin Diagnostic Breast Cancer (WDBC) with benign (n=21) and malignant (n=21) masses are collected from the kaggle machine learning repository for the proposed study. The dataset contains 21 attributes which are considered as inputs to our study. The classification of diseased and healthy subjects was performed using WEKA, a data mining tool. The statistical analysis was performed using IBM SPSS software. Results: The performance of the classifier was compared by an independent sample t-test using SPSS software. The statistical insignificant difference (p>0.05) was observed between the groups. The Adaboost classifier achieved the classification accuracy rate as 92.85%, which is higher than the Bagging classifier (85.71%). Conclusion: The Adaboost classifier shows the higher accuracy value when compared to the Bagging classifier for predicting the benign and malignant tumor cells using WDBC dataset.