Rakibul Hasan

Work place: Department of Computer Science and Engineering, Khwaja Yunus Ali University (KYAU), Enayetpur, Sirajganj-6751, Bangladesh

E-mail: rakib1997031@gmail.com

Website: https://orcid.org/0000-0002-2848-4460

Research Interests: Computer systems and computational processes, Computer Vision, Computer Architecture and Organization, Image Compression, Image Manipulation, Image Processing, Medical Image Computing, Data Mining, Data Structures and Algorithms


Rakibul Hasan received the B.Sc. degree in Computer Science and Engineering (CSE) from Khwaja Yunus Ali University, Sirajganj, Bangladesh. He is currently pursuing his Master of Science in Computer Science and Engineering from Mawlana Bhashani Science and Technology University, Tangail, Bangladesh. His area of research interests are Medical Image Processing, Machine/Deep learning, Computer Vision, and Data Mining.

Author Articles
Feature Selection based Breast Cancer Prediction

By Rakibul Hasan A. S. M. Shafi

DOI: https://doi.org/10.5815/ijigsp.2023.02.02, Pub. Date: 8 Apr. 2023

Breast cancer is one of the main causes of mortality for women around the world. Such mortality rate could be reduced if it is possible to diagnose breast cancer at the primary stage. It is hard to determine the causes of this disease that may lead to the development of breast cancer. But it is still important in predicting the probability of cancer. We can assess the likelihood of occurrence of breast cancer using machine learning algorithms and routine diagnosis data. Although a variety of patient information attributes are stored in cancer datasets not all of the attributes are important in predicting cancer. In such situations, feature selection approaches can be applied to keep the pertinent feature set. In this research, a comprehensive analysis of Machine Learning (ML) classification algorithms with and without feature selection on Wisconsin Breast Cancer Original (WBCO), Wisconsin Diagnosis Breast Cancer (WDBC), and Wisconsin Prognosis Breast Cancer (WPBC) datasets is performed for breast cancer prediction. We employed wrapper-based feature selection and three different classifiers Logistic Regression (LR), Linear Support Vector Machine (LSVM), and Quadratic Support Vector Machine (QSVM) for breast cancer prediction. Based on experimental results, it is shown that the LR classifier with feature selection performs significantly better with an accuracy of 97.1% and 83.5% on WBCO and WPBC datasets respectively. On WDBC datasets, the result reveals that the QSVM classifier without feature selection achieved an accuracy of 97.9% and these results outperform the existing methods.

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