International Journal of Computer Science & Network Security

국제컴퓨터통신보호논문지학회 (International Journal of Computer Science & Network Security)
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Hyperparameter Tuning Based Machine Learning classifier for Breast Cancer Prediction

  • Md. Mijanur Rahman (Dept. of Computer Science and Engineering, Southeast University) ;
  • Asikur Rahman Raju (Dept. of Computer Science and Engineering Southeast University) ;
  • Sumiea Akter Pinky (Dept. of Computer Science and Engineering Southeast University) ;
  • Swarnali Akter (Dept. of Computer Science and Engineering Southeast University)
  • 투고 : 2024.02.05
  • 발행 : 2024.02.29
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초록

Currently, the second most devastating form of cancer in people, particularly in women, is Breast Cancer (BC). In the healthcare industry, Machine Learning (ML) is commonly employed in fatal disease prediction. Due to breast cancer's favorable prognosis at an early stage, a model is created to utilize the Dataset on Wisconsin Diagnostic Breast Cancer (WDBC). Conversely, this model's overarching axiom is to compare the effectiveness of five well-known ML classifiers, including Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), K-Nearest Neighbor (KNN), and Naive Bayes (NB) with the conventional method. To counterbalance the effect with conventional methods, the overarching tactic we utilized was hyperparameter tuning utilizing the grid search method, which improved accuracy, secondary precision, third recall, and finally the F1 score. In this study hyperparameter tuning model, the rate of accuracy increased from 94.15% to 98.83% whereas the accuracy of the conventional method increased from 93.56% to 97.08%. According to this investigation, KNN outperformed all other classifiers in terms of accuracy, achieving a score of 98.83%. In conclusion, our study shows that KNN works well with the hyper-tuning method. These analyses show that this study prediction approach is useful in prognosticating women with breast cancer with a viable performance and more accurate findings when compared to the conventional approach.

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과제정보

We would like to use this chance to express our gratitude to the Center for Artificial Intelligence and Robotics (CAIR).

참고문헌

  1. Knowledge, Attitude and Practice of Bangladeshi Women towards Breast Cancer: A Cross Sectional Study. [Online]. Available: https://pubmed.ncbi.nlm.nih.gov/30755557/
  2. S. Roy, R. Gawande, A. Nawghare, and S. Mistry, "Comparative Study of Machine Learning Algorithms for Detecting Breast Cancer", International Journal of Computer Science Trends and Technology (IJCST), Vol. 9, No. 3, pp. 103-111, 2021.
  3. K. Hashi, E. and M.S. Zaman, "Developing a Hyperparameter Tuning Based Machine Learning Approach of Heart Disease Prediction", Journal of Applied Science & Process Engineering, Vol. 7, No. 2, pp. 631-647, 2020. https://doi.org/10.33736/jaspe.2639.2020
  4. D. Bazazeh, and R. Shubair, "Comparative study of machine learning algorithms for breast cancer detection and diagnosis", 2016 5th International Conference on Electronic Devices, Systems and Applications (ICEDSA), pp. 1-4, 2016.
  5. S. Chaurasiya, and R. Rajak, "Comparative Analysis of Machine Learning Algorithms in Breast Cancer Classification", pp. 1-12, 2022.
  6. T.A. Assegie, "An optimized K-Nearest Neighbor based breast cancer detection" Journal of Robotics and Control (JRC), Vol. 2, No. 3, pp. 115-118, 2021. https://doi.org/10.18196/jrc.2363
  7. N. Mashudi, S. Rossli, N.Ahmad, and N.M. Noor, "Comparison on Some Machine Learning Techniques in Breast Cancer Classification" 2020 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES), pp. 499-504, 2020.
  8. P. Guptaa, and S. Garga, "Breast Cancer Prediction using varying Parameters of Machine Learning Models" Third International Conference on Computing and Network Communications (CoCoNet'19), Vol. 171, pp. 593-601, 2020.
  9. S. Ara, A. Das, and A. Dey, "Malignant and Benign Breast Cancer Classification using Machine Learning Algorithms", 2021 International Conference on Artificial Intelligence (ICAI), pp. 97-101, 2021.
  10. M. Amrane, S. Oukid, I. Gagaoua, and T. ENSAR, "Breast Cancer Classification Using Machine Learning", 2018 Electric Electronics, Computer Science, Biomedical Engineerings' Meeting (EBBT), pp. 1-4, 2018.
  11. E. Merouane, A. Said, and E.F. Nour-eddine, "Prediction of Metastatic Relapse in Breast Cancer using Machine Learning Classifiers", (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 13, No. 2, pp. 176-181, 2022.
  12. M. Amrane, S. Oukid, I. Gagaoua, and T. ENSAR, "Breast Cancer Classification Using Machine Learning", 2018 Electric Electronics, Computer Science, Biomedical Engineerings' Meeting (EBBT), pp. 1-4, 2018.
  13. M.A. Aswathy, and M. Jagannath, "An SVM approach towards breast cancer classification from H&E-stained histopathology images based on integrated features", International Federation for Medical and Biological Engineering, pp. 1-11, 2021.
  14. P. Sengar, M. Gaikwad, and A. Nagdive, "Comparative Study of Machine Learning Algorithms for Breast Cancer Prediction" 2020 Third International Conference on Smart Systems and Inventive Technology (ICCSIT), pp. 796-801, 2020.
  15. B.R. Roy, M.; Pal, S. Das, and A. Huq, "Comparative Study of Machine Learning Approaches on Diagnosing Breast Cancer for Two Different Dataset", 2020 2nd International Conference on Advanced Information and Communication Technology (ICAICT), pp. 29-34, 2020.