Predicting Survival of Leukemia Patients Using a Support Vector Machine Based on the Bowerbird Algorithm

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 ۱۹۷ samples and ۹ 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 ۶۹.۵۷% accuracy, ۷۵.۵۲% sensitivity, and ۶۴.۴۸% 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.

Keywords:

cancer , Leukemia , Bowerbird Algorithm , Support Vector Machine