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RoughSet based Feature Selection for Prediction of Breast Cancer

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Abstract

Breast cancer is the most deadly cancer and has highest mortality rate in women all over the world. Early prediction of breast cancer can improve the survival rate of the patient. Consequently, high accuracy in cancer prediction is important to avoid any mis-diagnosis. Machine learning algorithms can contribute in early prediction and diagnosis of breast cancer. In this study, we have used rough set based feature selector to extract relevant features from the breast cancer feature set and classify them using machine learning algorithm like Decision Tree, Naive Bayes, Support Vector Machine, K-Nearest Neighbor, Logistic Regression, Random Forest, Adaboost. The main aim is to predict cancerous breast nodules, using rough set driven feature selection and machine learning classification algorithms. The results were evaluated pertaining to accuracy, sensitivity and specificity and positive predictive value. It is observed that random forest outperformed all other classifiers and achieved the highest accuracy using the proposed approach (95.23%).

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Data Availability

The datasets generated during the current study are available from the corresponding author on request.

Code Availability

The PYTHON codes used during the current study are available from the corresponding author on request.

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Authors and Affiliations

  1. Department of CSE, Kakatiya Institute of Technology and Science, Warangal, India

    Hanumanthu Bhukya & M Sadanandam

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  1. Hanumanthu Bhukya
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  2. M Sadanandam
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Correspondence to Hanumanthu Bhukya.

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Bhukya, H., Sadanandam, M. RoughSet based Feature Selection for Prediction of Breast Cancer. Wireless Pers Commun 130, 2197–2214 (2023). https://doi.org/10.1007/s11277-023-10378-4

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