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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Md. Toukir Ahmed & Md. Rayhanul Masud & Abdullah Al Mamun, 2020. "Comparisons Among Multiple Machine Learning Based Classifiers for Breast Cancer Risk Stratification Using Electrical Impedance Spectroscopy," European Journal of Electrical Engineering and Computer Science, European Open Science, vol. 4(4), July.
    • Handle: RePEc:epw:ejece0:v:4:y:2020:i:4:id:19227
    DOI: 10.24018/ejece.2020.4.4.227
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