Assessing the Performance of Data Mining Techniques for Breast Cancer Patient Screening
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Abstract
This research aimed to evaluate the performance of various data mining techniques in constructing predictive models for breast cancer screening. Seven classification methods were compared, namely Neural networks, Support Vector Machine (SVM), Naïve Bayes, k-Nearest Neighbors (k-NN), Decision tree, Deep learning, and Ensemble vote. The dataset used in this study comprised 569 patient records obtained from the University of Wisconsin and made publicly available on www.kaggle.com. The analysis was conducted following the CRISP-DM process, which included variable selection, handling of missing data, and defining the roles of each attribute. The results revealed that the Neural Network technique yielded the best performance, achieving an accuracy of 98.07%, sensitivity of 99.15%, specificity of 96.21%, and an overall efficiency of 98.47%. These findings demonstrate the potential of this technique to significantly support the early detection and diagnosis of breast cancer.
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