Unfortunately, research has shown that cancer incidence has been increasing in recent years. Leukemia is a type of blood cancer caused by an increase in the number of white blood cells. Generally, any type of blood cancer is extremely dangerous, and in most cases, there is no cure. Acute myeloid leukemia (AML) is a common and fatal type of this cancer. Predicting survival after diagnosis is one of the key indicators for evaluating treatment methods, which is the focus of the present study. In this research, we used a combination of Support Vector Machine (SVM) with the Bowerbird Algorithm to analyze survival status and predict mortality in patients. The data used pertains to patient information from Seyyed al-Shohada Hospital in Isfahan, with 197 samples and 9 features. MATLAB software was used to run the programs. Evaluation was based on diagnostic indices including sensitivity, specificity, and accuracy. The proposed SVM based on the Bowerbird Algorithm achieved a performance of 69.57% accuracy, 75.52% sensitivity, and 64.48% specificity, outperforming the combination of SVM with other optimization algorithms such as Cuckoo Search, Harmony Search, and Firefly Algorithm. Therefore, the proposed method is a promising tool for predicting survival in leukemia patients with improved diagnostic accuracy.
Hajizadeh, E., & Haji Fathali, A. (2020). Mixture and non-mixture cured models in survival analysis of leukemia patients: A cohort study. Studies in Medical Sciences, 31(10), 802-812.
Najafi, R., Olfatifar, M., Razi, M., & Amiri, F. (2020). Analyzing the survival of patients with breast cancer with a multistate model. Iranian Quarterly Journal of Breast Disease, 13(2), 19-27. https://doi.org/10.30699/ijbd.13.2.19
Safdarian, N., & Yousefian Dezfoulinejad, S. (2020). Mammographic image processing for classification of breast cancer masses by using support vector machine method and grasshopper optimization algorithm. Journal of Arak University of Medical Sciences, 23(2), 246-263. https://doi.org/10.32598/JAMS.23.2.4672.1
Fradkin, D., & Muchnik, I. (2006). Support vector machines for classification. DIMACS Series in Discrete Mathematics and Theoretical Computer Science, 70, 13-20.
Akrimi, J. A., Suliman, A., George, L. E., & Ahmad, A. R. (2014). Classification of red blood cells using support vector machine. In 2014 International Conference on Information Technology and Multimedia (ICIMU) (pp. 265-269). IEEE. https://doi.org/10.1109/ICIMU.2014.7066642
Bai, G. M., & Venkadesh, P. (2021). Taylor-monarch butterfly optimization-based support vector machine for acute lymphoblastic leukemia classification with blood smear microscopic images. Journal of Mechanics in Medicine and Biology, 21(06), 2150041. https://doi.org/10.1142/S021951942150041X
Jusman, Y., Hasanah, A. N., Purwanto, K., Kanafiah, S. N. A. M., Riyadi, S., Hassan, R., & Mohamed, Z. (2021). Comparison between support vector machine and K-nearest neighbor algorithms for leukemia images classification using shape features. In 2021 25th International Computer Science and Engineering Conference (ICSEC) (pp. 70-74). IEEE. https://doi.org/10.1109/ICSEC53205.2021.9684585
Hosseini Tashnizi, S., Tehrani, M., & Toosali Farhi, M. (2013). Comparison of Cox regression model and artificial neural network in predicting survival of leukemia patients. Iranian Journal of Epidemiology, 9(2), 71-92.
Rawat, J., Singh, A., Bhadauria, H. S., Virmani, J., & Devgun, J. S. (2017). Computer-assisted classification framework for prediction of acute lymphoblastic and acute myeloblastic leukemia. Biocybernetics and Biomedical Engineering, 37(4), 637-654. https://doi.org/10.1016/j.bbe.2017.07.003
Hosseinzadeh Kashan, A., & Garoosi, F. (2020). Hybrid model binary ant colony algorithm and support vector machine (BACO-SVM) for feature selection and classification of bank customers with case study. Financial Management Strategy, 8(2), 71-92.
Burges, C. J. (1998). A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2(2), 121-167. https://doi.org/10.1023/A:1009715923555
Hosseinzadeh Kashan, A., & Garoosi, F. (2020). Hybrid model binary ant colony algorithm and support vector machine (BACO-SVM) for feature selection and classification of bank customers with case study. Financial Management Strategy, 8(2), 71-92.
Moosavi, S. H. S., & Bardsiri, V. K. (2017). Satin bowerbird optimizer: A new optimization algorithm to optimize ANFIS for software development effort estimation. Engineering Applications of Artificial Intelligence, 60, 1-15. https://doi.org/10.1016/j.engappai.2017.01.006
Marousi,A. and Marousi,F. (2021). Predicting Survival of Leukemia Patients Using a Support Vector Machine Based on the Bowerbird Algorithm. Transactions on Machine Intelligence, 4(2), 63-69. doi: 10.47176/TMI.2021.63
MLA
Marousi,A. , and Marousi,F. . "Predicting Survival of Leukemia Patients Using a Support Vector Machine Based on the Bowerbird Algorithm", Transactions on Machine Intelligence, 4, 2, 2021, 63-69. doi: 10.47176/TMI.2021.63
HARVARD
Marousi A., Marousi F. (2021). 'Predicting Survival of Leukemia Patients Using a Support Vector Machine Based on the Bowerbird Algorithm', Transactions on Machine Intelligence, 4(2), pp. 63-69. doi: 10.47176/TMI.2021.63
CHICAGO
A. Marousi and F. Marousi, "Predicting Survival of Leukemia Patients Using a Support Vector Machine Based on the Bowerbird Algorithm," Transactions on Machine Intelligence, 4 2 (2021): 63-69, doi: 10.47176/TMI.2021.63
VANCOUVER
Marousi A., Marousi F. Predicting Survival of Leukemia Patients Using a Support Vector Machine Based on the Bowerbird Algorithm. Trans. Mach. Intell., 2021; 4(2): 63-69. doi: 10.47176/TMI.2021.63
