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All Journal Bulletin of Electrical Engineering and Informatics Bulletin of Electrical Engineering and Informatics Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Mohd Najib Mohd Salleh
Universiti Tun Hussein Onn Malaysia
Computer Science & IT Electrical & Electronics Engineering Engineering

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Journal : Bulletin of Electrical Engineering and Informatics

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Comparative analysis on bayesian classification for breast cancer problem Wan Nor Liyana Wan Hassan Ibeni; Mohd Zaki Mohd Salikon; Aida Mustapha; Saiful Adli Daud; Mohd Najib Mohd Salleh
Bulletin of Electrical Engineering and Informatics Vol 8, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (585.991 KB) | DOI: 10.11591/eei.v8i4.1628

Abstract

The problem of imbalanced class distribution or small datasets is quite frequent in certain fields especially in medical domain. However, the classical Naive Bayes approach in dealing with uncertainties within medical datasets face with the difficulties in selecting prior distributions, whereby parameter estimation such as the maximum likelihood estimation (MLE) and maximum a posteriori (MAP) often hurt the accuracy of predictions. This paper presents the full Bayesian approach to assess the predictive distribution of all classes using three classifiers; naïve bayes (NB), bayesian networks (BN), and tree augmented naïve bayes (TAN) with three datasets; Breast cancer, breast cancer wisconsin, and breast tissue dataset. Next, the prediction accuracies of bayesian approaches are also compared with three standard machine learning algorithms from the literature; K-nearest neighbor (K-NN), support vector machine (SVM), and decision tree (DT). The results showed that the best performance was the bayesian networks (BN) algorithm with accuracy of 97.281%. The results are hoped to provide as base comparison for further research on breast cancer detection. All experiments are conducted in WEKA data mining tool.
Co-Authors Aida Mustapha Haseeb Ali Kashif Hussain Mohd Zaki Mohd Salikon Muhammad Faheem Mushtaq Rd. Rohmat Saedudin Saiful Adli Daud Wan Nor Liyana Wan Hassan Ibeni