Diterbitkan Oleh:
Lembaga Penelitian Pengabdian Masyarakat Universitas Nusa Mandiri
Creation is distributed below Lisensi Creative Commons Atribusi-NonKomersial 4.0 Internasional.
DATA MINING USING RANDOM FOREST, NAÏVE BAYES, AND ADABOOST MODELS FOR PREDICTION AND CLASSIFICATION OF BENIGN AND MALIGNANT BREAST CANCER
Keywords:
prediction, data mining, classification, breast cancer, model classification
Abstract
This study predicts and classifies benign and malignant breast cancer using 3 classification models. The method used in this research is Random Forest, Naïve Bayes and AdaBoost. The prediction results get Random Forest = 100%, Naïve Bayes = 80% and AdaBoost = 80%. Results using Test and Score with Number of Folds 2, 5 and 10. Number of Folds 2 Random Forest model Accuracy = 95%, Precision = 95% and Recall = 95%, Naïve Bayes Accuracy = 93%, Precision = 93% and Recall 93%, AdaBoost Accuracy = 90%, Precision = 90% and Recall = 90%. With Number of Folds 5 with Random Forest = 96%, Precision = 96% and Recall 96%. Naïve Bayes Accuracy value = 94%, Precision = 94% and Recall = 94%, AdaBoost Accuracy value = 93%, Precision = 93% and Recall = 93%. With Number of Folds 10 Random Forest model = 96%, Precision = 96% and Recall 96%. Naïve Bayes Accuracy value = 94%, Precision = 94% and Recall = 94%, AdaBoost Accuracy value = 92%, Precision = 92% and Recall = 92%. Of the 3 models used, Random Forest got the best classification results compared to the others.
Downloads
Download data is not yet available.
References
Abd-Elrazek, M. A., Othman, A. A., Abd Elaziz, M. H., & Abd-Elwhab, M. N. (2018). Intelligent Prediction of Breast Cancer: A Comparative Study. Egyptian Computer Science Journal, 42(3), 29–43.
Anjum, A. (2019). Role of Machine Learning in Diagnosis of Breast Cancer. International Journal of Innovative Science and Research Technology, 4(5), 280–284.
Ayyoubzadeh, S. M., Almasizand, A., & ... (2021). Early Breast Cancer Prediction Using Dermatoglyphics: Data Mining Pilot Study in a General Hospital in Iran. Health Education and Health Promotion, 9(3), 279–285.
Bissanum, R., Chaichulee, S., Kamolphiwong, R., Navakanitworakul, R., & Kanokwiroon, K. (2021). Molecular classification models for triple negative breast cancer subtype using machine learning. Journal of Personalized Medicine, 11(9). https://doi.org/10.3390/jpm11090881
BV, E. (2019). Application of Data Mining Techniques to Predict Breast Cancer. Procedia Computer Science.
Chaurasia, V., Pal, S., & Tiwari, B. B. (2018). Prediction of benign and malignant breast cancer using data mining techniques. Journal of Algorithms and Computational Technology, 12(2), 119–126. https://doi.org/10.1177/1748301818756225
Ganggayah, M. D., Taib, N. A., Har, Y. C., Lio, P., & Dhillon, S. K. (2019). Predicting factors for survival of breast cancer patients using machine learning techniques. BMC Medical Informatics and Decision Making, 19(1), 1–17. https://doi.org/10.1186/s12911-019-0801-4
Gupta, A., & Kaushik, B. N. (2018). Feature selection from biological database for breast cancer prediction and detection using machine learning classifier. Journal of Artificial Intelligence, 11(2), 55–64. https://doi.org/10.3923/jai.2018.55.64
Jean Sunny, Nikita Rane, Rucha Kanade, & Sulochana Devi. (2020). Breast Cancer Classification and Prediction using Machine Learning. International Journal of Engineering Research And, V9(02), 576–580. https://doi.org/10.17577/ijertv9is020280
Kaur, E. R., & Chopra, V. (2015). Implementing Adaboost and Enhanced Adaboost Algorithm in Web Mining. International Journal of Adanced Research in Computer and Communication Engineering, 4(7), 306–311. https://doi.org/10.17148/IJARCCE.2015.4771
Khan, I., Gandhi, A., Parmar, N., & Garg, B. (2020). Breast cancer prediction using data mining. International Journal of Scientific Research and Engineering Development, 3(2), 978–980.
Kharya, S., & Soni, S. (2016). Weighted Naive Bayes Classifier: A Predictive Model for Breast Cancer Detection. International Journal of Computer Applications, 133(9), 32–37. https://doi.org/10.5120/ijca2016908023
Kodati, S., & Vivekanandam, R. (2018). Analysis of Heart Disease using in Data Mining Tools Orange and Weka. Global Journal of Computer Science and Technology: C Software & Data Engineering, 18(1), 16–22.
Krishna, M. H., & Rao, D. K. N. (2018). PREDICTION OF BREAST CANCER USING MACHINE LEARNING TECHNIQUES. International Journal of Management, Technology And Engineering, 8(12), 150–153. https://doi.org/10.2174/2213275912666190617160834
Kukasvadiya, M. S., Nidhi, D., & Divecha, H. (2017). Analysis of Data Using Data Mining tool Orange. International Journal of Engineering Development and Research, 5(2), 1836–1840.
Kumari, G. T. P., & Rani, M. U. (2017). A Study of AdaBoost and Bagging Approaches on Student Dataset. Journal of Advanced Engineering and Science, 2(2), 375–380.
Li, J., Zhou, Z., Dong, J., Fu, Y., Li, Y., Luan, Z., & Peng, X. (2021). Predicting breast cancer 5-year survival using machine learning: A systematic review. PLoS ONE, 16(4 April), 1–23. https://doi.org/10.1371/journal.pone.0250370
Manimannan, G., Priya, R. L., & Kumar, C. A. (2019). Application of Orange Data Mining Approach of Argiculture Productivity Index Performance in Tamilnadu. International Journal of Scientific and Innovative Mathematical Research, 7(8), 8–16. https://doi.org/10.20431/2347-3142.0708003
Masood, H. (2021). Breast Cancer Detection Using Machine Learning Algorithms. International Research Journal of Engineering and Technology (IRJET), 8(2), 738–747. https://doi.org/10.1007/978-981-16-6309-3_34
More, A., Mhatre, S., Kamble, V., Patil, V., & Bhairnallykar, S. (2022). Breast Cancer Prediction Using Classification Techniques of Machine Learning. International Journal for Research in Applied Science & Engineering Technology (IJRASET), 10(1). https://doi.org/10.26483/ijarcs.v10i5.6464
Nindrea, R. D., Aryandono, T., Lazuardi, L., & Dwiprahasto, I. (2018). Diagnostic accuracy of different machine learning algorithms for breast cancer risk calculation: A meta-analysis. Asian Pacific Journal of Cancer Prevention, 19(7), 1747–1752. https://doi.org/10.22034/APJCP.2018.19.7.1747
Nitasha. (2019). Review on Breast Cancer Prediction Using Data Mining Algorithms. International Journal of Computer Science Trends and Technology (IJCST), 7(4), 42–45.
Octaviani, T. L., & Rustam, Z. (2019). Random forest for breast cancer prediction. AIP Conference Proceedings, 2168(November). https://doi.org/10.1063/1.5132477
Perveen, S., Shahbaz, M., Guergachi, A., & Keshavjee, K. (2016). Performance Analysis of Data Mining Classification Techniques to Predict Diabetes. Procedia Computer Science, 82(March), 115–121. https://doi.org/10.1016/j.procs.2016.04.016
Rajendran, K., Jayabalan, M., & Thiruchelvam, V. (2020). Predicting breast cancer via supervised machine learning methods on class imbalanced data. International Journal of Advanced Computer Science and Applications, 11(8), 54–63. https://doi.org/10.14569/IJACSA.2020.0110808
Rawal, R. (2020). BREAST CANCER PREDICTION USING MACHINE LEARNING. Journal of Emerging Technologies and Innovative Research (JETIR), 7(5), 13–24. https://doi.org/10.2478/acss-2020-0018
Sasikala, R. (2017). a Comparative Analysis for Smart Water Resource Using Data Mining Tools. International Journal of Research -GRANTHAALAYAH, 5(7(SE)), 24–30. https://doi.org/10.29121/granthaalayah.v5.i7(se).2017.2039
Singh, H. (2021). Breast Cancer Analysis and Prediction by Using Machine Learning. International Journal of Research in Engineering and Science (IJRES), 9(6), 69–73.
Swetha, K., & Ranjana, R. (2020). Breast Cancer Predication Using Machine Learning and Data Mining. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 6(3), 610–615.
Published
2022-03-09
How to Cite
Imran, B., Hambali, H., Subki, A., Zaeniah, Z., Yani, A., & Alfian, M. (2022). DATA MINING USING RANDOM FOREST, NAÏVE BAYES, AND ADABOOST MODELS FOR PREDICTION AND CLASSIFICATION OF BENIGN AND MALIGNANT BREAST CANCER. Jurnal Pilar Nusa Mandiri, 18(1), 37-46. https://doi.org/10.33480/pilar.v18i1.2912
Section
Articles
Copyright (c) 2022 Bahtiar Imran, Hambali Hambali, Ahmad Subki, Zaeniah Zaeniah, Ahmad Yani, Muhammad Rijal Alfian
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
An author who publishes in the Pilar Nusa Mandiri: Journal of Computing and Information System agrees to the following terms:
- Author retains the copyright and grants the journal the right of first publication of the work simultaneously licensed under the Creative Commons Attribution-NonCommercial 4.0 License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal
- Author is able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book) with the acknowledgement of its initial publication in this journal.
- Author is permitted and encouraged to post his/her work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of the published work (See The Effect of Open Access).
Read more about the Creative Commons Attribution-NonCommercial 4.0 Licence here: https://creativecommons.org/licenses/by-nc/4.0/.
