Comparisons Among Multiple Machine Learning Based Classifiers for Breast Cancer Risk Stratification Using Electrical Impedance Spectroscopy

Md. Toukir Ahmed, Md. Rayhanul Masud and Abdullah Al Mamun
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Md. Toukir Ahmed: Pabna University of Science and Technology, Bangladesh
Md. Rayhanul Masud: Bangladesh University of Engineering and Technology, Bangladesh
Abdullah Al Mamun: Bangladesh University of Engineering and Technology, Bangladesh

European Journal of Electrical Engineering and Computer Science, 2020, vol. 4, issue 4

Abstract: Nowadays, women worldwide are affected greatly by Breast cancer. The consequences of the disease and the number of affected are so alarming that it requires immediate attention. Prediction of the disease is the primary and most significant route to prevention of it. This study aims to have a comparison among multiple machine learning based classifiers for breast cancer risk stratification using resonance-frequency electrical impedance spectroscopy. Five machine learning based classifiers namely- Naïve Bayes, Multilayer perceptron, J48, Bagging and Random Forest were applied to the dataset and a comparison was made based on different performance metrics. The study demonstrated that Random Forest classifier performed slightly better than the others in both splitting and folding of the dataset.

Keywords: Breast Cancer; Classifier; Machine Learning; Spectroscopy (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:epw:ejece0:v:4:y:2020:i:4:id:19227

DOI: 10.24018/ejece.2020.4.4.227

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