Comparative Evaluation of Machine Learning Algorithms in Breast Cancer

Bikhtiyar Friyad Abdulrahman; Roojwan Sc Hawezi; Saja Mohammed Noori M.R; Zhwan mohammed khalid; Shahab Wahhab Kareem; Zanyar Rzgar Ahmed

doi:10.25212/lfu.qzj.7.1.34

المؤلفون

Bikhtiyar Friyad Abdulrahman Department of Technical Information Systems Engineering, College of Erbil Technical Engineering, Erbil Polytechnic University, Erbil, Kurdistan Region, Iraq
Roojwan Sc Hawezi Department of Technical Information Systems Engineering, College of Erbil Technical Engineering, Erbil Polytechnic University, Erbil, Kurdistan Region, Iraq
Saja Mohammed Noori M.R Department of Computer Network, College of Engineering and Computer Science, Lebanese French University, Erbil, Kurdistan Region, Iraq
Zhwan mohammed khalid Department of Technical Information Systems Engineering, College of Erbil Technical Engineering, Erbil Polytechnic University, Erbil, Kurdistan Region, Iraq
Shahab Wahhab Kareem Department of Technical Information Systems Engineering, College of Erbil Technical Engineering, Erbil Polytechnic University, Erbil, Kurdistan Region, Iraq
Zanyar Rzgar Ahmed Department of Information Technology, College of Engineering and Computer Science, Lebanese French University, Erbil, Kurdistan Region, Iraq

DOI:

https://doi.org/10.25212/lfu.qzj.7.1.34

الكلمات المفتاحية:

Machine Learning, Classification algorithms, Neural Network, Breast Cancer, Dataset

الملخص

Breast cancer is one of the world's leading causes of mortality in women and is due to uncontrollable breast cell growth. Early detection and proper care are the only means of avoiding deaths from breast cancer. The precise characterization of tumors is a critical task in the medical profession. Because of their high precision and accuracy, machine learning methods are commonly used in identifying and classifying various forms of cancer. In this review article, the authors have tested different machine learning algorithms and implemented them, which can be used by doctors to identify cancer cells in an early and accurate way. This article introduces several algorithms, including support vector machine (SVM), Nave Bayes Classifier (NBC), artificial neural network (ANN), Random Forest (RF), decision tree (DT), and k-Nearest-Neighbor (KNN). These algorithms are trained with a collection of data containing tumor parameters for a person with breast cancer. After comparing the results, we found the highest accuracy of the Support Vector Machine and Random Forest and the highest precision of the Naive Bayes Classifier (NBC). In addition, we review the number of researches that provide machine learning algorithms for detecting breast cancer

التنزيلات

بيانات التنزيل غير متوفرة بعد.

المراجع

A. Bharathi and K. Anandakumar. (2015). Cancer classification using relevance vector machine learning approach. Journal of medical imaging and health informatics, vol. 5, 630-634.

A. S. Mohammed, S. W. Kareem, A. Al Azzawi, and M. Sivaram. (2018). Time series prediction using SRE-NAR and SRE-ADALINE. Journal of Advanced Research in Dynamical and Control Systems , 1716-1726.

A. T. Azar and S. A. El-Said. (2013). Probabilistic neural network for breast cancer classification. Neural Computing and Applications, vol. 23, 1737-1751.

Al-Alkawi, H. J. M., Hanfesh, A. O., & Mohammed Rauof, S. M. N. (2019). Development of High Performance (Mechanical and Wear Properties) of AA 6061-Hybrid Nano Composites Via Liquid Metallurgy Route. Al-Nahrain Journal for Engineering Sciences, 22(2), 143.

Alyousuf, F. Q. A., Din, R., & Qasim, A. J. (2020). Analysis review on spatial and transform domain technique in digital steganography. Bulletin of Electrical Engineering and Informatics, 9(2), 573-581.

Ashish Sharma, A. M.-1.-6. (2022). The 3D Facemask Recognition: Minimization for Spreading COVID-19 and Enhance Security, . ICT Analysis and Applications, Springer Singapore 2022 P 619-637 (pp. 619-637). Singapore : Springer .

B. Gayathri and C. Sumathi. (2015). Mamdani fuzzy inference system for breast cancer risk detection . B. Gayathri and C. Sumathi, "Mamdani fuzzy inference system for bre IEEE International Conference on Computational Intelligence and Computing Research (ICCIC) (pp. 1-6). IEEE.

C. Nguyen, Y. Wang, and H. N. Nguyen. (2013). Random forest classifier combined with feature selection for breast cancer diagnosis and prognostic . Journal of Biomedical Science and Engineering, 551-560.

C. S. Park, S. H. Kim, N. Y. Jung, J. J. Choi, B. J. Kang, and H. S. Jung. (2015). Interobserver variability of ultrasound elastography and the ultrasound BI-RADS lexicon of breast lesions . Breast Cancer, vol. 22, 153-160.

D. Bazazeh and R. Shubair. (2016). Comparative study of machine learning algorithms for breast cancer detection and diagnosis. D. Bazazeh and R. Shubair, "Comparative study of machine learning algorithms for breast cancer detec5th international conference on electronic devices, systems and applications (ICEDSA) , (pp. 1-4).

E. Alickovic and A. Subasi. (2019). Normalized neural networks for breast cancer classification. International conference on medical and biological engineering, (pp. 519-524).

E. Venkatesan and T. Velmurugan. (2015). Performance analysis of decision tree algorithms for breast cancer classification. Indian Journal of Science and Technology, vol. 8, 1-8.

F. Cugnata, R. S. Kenett, and S. Salini. (2016). Bayesian networks in survey data: Robustness and sensitivity issues. Journal of Quality Technology, vol. 48, 253-264.

H. T. T. Thein and K. M. M. Tun. (2015). An approach for breast cancer diagnosis classification using neural network. Advanced Computing, vol. 6, 1-8.

H. Wang, B. Zheng, S. W. Yoon, and H. S. Ko. (2018). A support vector machine-based ensemble algorithm for breast cancer diagnosis. European Journal of Operational Research, vol. 267, 687-699.

Hota, H. (vol. 3, 2014.). Identification of breast cancer using ensemble of support vector machine and decision tree with reduced feature subset. H. Hota, "Identification of breast cancer using ensemble of support vector machine and decision tree International Journal of Innovative Technology and Exploring Engineering , 99-102.

I. Routray and N. P. Rath. (2018). Textural feature based classification of mammogram images using ANN. 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT), (pp. 1-6).

J. Wu and C. Hicks. (2021). Breast Cancer Type Classification Using Machine Learning. Journal of personalized medicine, vol. 11, 61-70.

K. Kourou, T. P. Exarchos, K. P. Exarchos, M. V. Karamouzis, and D. I. Fotiadis. (2015). K. Kourou, T. P. Exarchos, K. P. Exarchos, M. V. KaramMachine learning applications in cancer prognosis and prediction. Computational and structural biotechnology journal, vol. 13 , 8-17.

K. Sivakami and N. Saraswathi. (2015.). Mining big data: breast cancer prediction using DT-SVM hybrid model. K. Sivakami and N. Saraswathi, "Mining big data: International Journal of Scientific Engineering and Applied Science (IJSEAS), vol. 1, K. Sivakami and N. Saraswathi, "Mining big data: breast cancer prediction using DT-SVM418-429.

K. Vijayakumar, V. J. Kadam, and S. K. Sharma. (2021). Breast cancer diagnosis using multiple activation deep neural network. Concurrent Engineering, vol. 29, 275-284.

Kareem, S. O. (2021). Falcon Optimization Algorithm For Bayesian Network Structure Learning. Computer Science Journal, Vol 22, No.4. , 553-569.

Kareem, S. W. (2009). Hybrid Public Key Encryption Algorithms For E-Commerce. Erbil: University of Salahaddin–Hawler.

KAREEM, S. W. (2020). NOVEL SWARM INTELLIGENCE ALGORITHMS FOR STRUCTURE LEARNING OF BAYESIAN NETWORKS AND A COMPARATIVE EVALUATION. IZMIR: YAŞAR UNIVERSITY.

Karplus, A. (2012). Machine learning algorithms for cancer diagnosis . Santa Cruz County Science Fair.

Kh., T. & Hamarash, I. (2022). Model-Based Quality Assessment of Internet of Things Software Applications: A Systematic Mapping Study. International Association of Online Engineering. Retrieved March.

Kononenko, I. (2001). Machine learning for medical diagnosis: history, state of the art and perspective. Artificial Intelligence in medicine, vol. 23, 89-109.

L. G. Ahmad, A. Eshlaghy, A. Poorebrahimi, M. Ebrahimi, and A. Razavi. (2013). L. G. Ahmad, A. Eshlaghy, A. PoorebrahUsing three machine learning techniques for predicting breast cancer recurrence. J Health Med Inform, vol. 4, p. 3, 2013., 3.

M. K. Hasan, M. M. Islam, and M. Hashem. (2016). Mathematical model development to detect breast cancer using multigene genetic programming. 5th International Conference on Informatics, Electronics and Vision (ICIEV), (pp. 574-579).

M. M. Islam, M. R. Haque, H. Iqbal, M. M. Hasan, M. Hasan, and M. N. Kabir. (vol. 1,2020.). Breast cancer prediction: a comparative study using machine learning techniques. SN Computer Science, vol. 1, 1-14.

M. Mori, S. Akashi-Tanaka, S. Suzuki, M. I. Daniels, C. Watanabe, M. Hirose, et al. (2017). M. Mori, S. Akashi-Tanaka, S. SDiagnostic accuracy of contrast-enhanced spectral mammography in comparison to conventional full-field digital mammography in a population of women with dense breasts," . reast Cancer, vol. 24 , 104-110 .

M. R. Haque, M. M. Islam, H. Iqbal, M. S. Reza, and M. K. Hasan. (2018). Performance evaluation of random forests and artificial neural networks for the classification of liver disorder. international conference on computer, communication, chemical, material and electronic engineering (IC4ME2), (pp. 1-5).

M. Rana, P. Chandorkar, A. Dsouza, and N. Kazi. (2015). Breast cancer diagnosis and recurrence prediction using machine learning techniques. IJRET: International Journal of Research in Engineering and Technology, 2319-1163.

M. Sardar, K. Al-Jumur, and S. W. Kareem, Raghad z. yousif. (2021). Predicting temperature of Erbil city applying deep learning and neural network. Indonesian Journal of Electrical Engineering and Computer Science, 944-952.

Miran, A., & Kadir, G. (2019c). Enhancing AODV routing protocol to support QoS. International Journal of Advanced Trends in Computer Science and Engineering (IJATCSE), 8(5), 1824–1830.

M.V. Prakash, V. Porkodi, S. Rajanarayanan, M. Khan, B.F. Ibrahim, M. Sivaram. (2020). Improved conservation of energy in fog IoT services using machine learning model. International Conference on Computing and Information Technology (ICCIT-1441), (pp. 1-4).

Mohammed, A. G. (2021). A Study of Scheduling Algorithms in LTE -Advanced HetNet. QZJ, vol. 6, no. 3, , 945–968

N. Louridi, M. Amar, and B. El Ouahidi. (2019). Identification of Cardiovascular Diseases Using Machine Learning. 7th Mediterranean Congress of Telecommunications (CMT) (pp. 1-6). : CMT.

N. Mallios, E. Papageorgiou, and M. Samarinas. (2011). N. Mallios, E. PapageComparison of machine learning techniques using the WEKA environment for prostate cancer therapy plan. IEEE 20th International Workshops on Enabling Technologies: Infrastructure for Collaborative Enerprises (pp. 151-155). IEEE .

P. Misra and A. S. Yadav. (2019). Impact of Preprocessing Methods on Healthcare Predictions. 2nd International Conference on Advanced Computing and Software Engineering (ICACSE).

P. Suryachandra and P. V. S. Reddy. (2016). Comparison of machine learning algorithms for breast cance. nternational Conference on Inventive Computation Technologies (ICICT), (pp. 1-6).

Qasim, A.J., Din, R.E., Alyousuf, F.Q.A. (2020). Review on techniques and file formats of image compression. Bull. Electr. Eng. Inf. 9(2), 602–610.

R. Alyami, J. Alhajjaj, B. Alnajrani, I. Elaalami, A. Alqahtani, N. Aldhafferi, et al. (2017). R. Alyami, J. Alhajjaj, B. Alnajrani, I. Elaalami, A. Alqahtani, N. Aldhafferi, et al., "Investigating the effect of correlation based feature selection on breast cancer diagnosis using artificial neural network and support vector machines. International Conference on Informatics, Health & Technology (ICIHT), (pp. 1-7).

R. S. Ismael, R. S. Youail, and S. W. Kareem. (2014). Image encryption by using RC4 algorithm . European Academic Research, II, vol. 4 , 5833-5839 .

Raghad Z. Yousif1, Shahab W. Kareem, Shadan M. Abdalwahid. (2020). Enhancing Approach for Information Security in Hadoop. Polytechnic Journal. 2020., 10(1), 808.

Syah, R., Davarpanah, A., Nasution, M. K., Wali, Q., Ramdan, D., Albaqami, M. D., ... & Noori, S. M. (2021). The Effect of Structural Phase Transitions on Electronic and Optical Properties of CsPbI3 Pure Inorganic Perovskites. Coatings, 11(10), 1173.

S. B. Sakri, N. B. A. Rashid, and Z. M. Zain. (2018). Particle swarm optimization feature selection for breast cancer recurrence prediction. IEEE Access, vol. 6, 29637-29647.

S. Chaurasia, P. Chakrabarti, and N. Chourasia. (vol. 59, 2012.). An application of classification techniques on breast cancer prognosis. International Journal of Computer Applications .

S. H. Ismael, S. W. Kareem, and F. H. Almukhtar. (2020). Medical Image Classification Using Different Machine Learning Algorithms. AL-Rafidain Journal of Computer Sciences and Mathematics, vol. 14, 135-147.

S. I. Ayon and M. Islam . (2019). Diabetes Prediction: A Deep Learning Approach . nternational Journal of Information Engineering & Electronic Business, vol. 11.

S. K. Sarkar and A. Nag. (2017). Identifying patients at risk of breast cancer through decision trees . International Journal of Advanced Research in Computer Science, vol. 8 , 88-91.

S. Kareem and M. C. Okur. (2017). Evaluation Of Bayesian Network Structure Learning . 2nd International Mediterranean Science and Engineering Congress (IMSEC 2017). Adana.

S. Kareem and M. C. Okur. (2018). Bayesian Network Structure Learning Using Hybrid Bee Optimization and Greedy Search . Çukurova University, 2018 (p. ). Adana: Çukurova University.

S. Kharya and S. Soni. (2016). Weighted naive bayes classifier: a predictive model for breast cancer detection. S. Kharya and S. Soni, "Weighted naive bayes classifier: a predictInternational Journal of Computer Applications, vol. 133, 32-37.

S. Kharya, D. Dubey, and S. Soni. (2013). Predictive machine learning techniques for breast cancer detection. International Journal of Computer Science and Information Technologies, vol. 4,, 1023-1028.

S. W. Kareem and M. C. Okur. (2019). Pigeon inspired optimization of bayesian network structure learning and a comparative evaluation. Journal of Cognitive Science, vol. 20, 535-552.

S. W. Kareem and M. C. Okur. (2020). Evaluation of Bayesian Network Structure Learning Using Elephant Swarm Water Search Algorithm. In S. W. Kareem and M. C. Okur, "Evaluation of BayesiHandbook of Research on Advancements of Swarm Intelligence Algorithms for Solving Real-World Problems. IGI.

T. Nagashima, M. Suzuki, H. Yagata, H. Hashimoto, T. Shishikura, N. Imanaka, et al. (2002). T. Nagashima, M. Suzuki, H. Yagata, H. Hashimoto, T. ShishDynamic-enhanced MRI predicts metastatic potential of invasive ductal breast cancer. Breast Cancer, vol. 9, 226.

W. Kareem, R. Z. Yousif, and S. M. J. Abdalwahid. (2020). An approach for enhancing data confidentiality in Hadoop. W. Kareem, R. Z. Yousif, and S. M. J. Abdalwahid, "An approach for enhancing data conIndonesian Journal of Electrical Engineering and Computer Science , 1547-1555.

W. Yue, Z. Wang, H. Chen, A. Payne, and X. Liu. (2018). Machine learning with applications in breast cancer diagnosis and prognosis. Designs, vol. 2, 13-20.

Y. Tan, K. Sim, and F. Ting. (2017). Breast cancer detection using convolutional neural networks for mammogram imaging system. International Conference on Robotics, Automation and Sciences (ICORAS), (pp. 1-5).

Y. Yasui and X. Wang. (2009). Statistical Learning from a Regression Perspective by BERK, RA. Wiley Online Library.