Adaptive Dynamic Dipper Throated Optimization for Feature Selection in Medical Data  

作　　者：Ghada Atteia El-Sayed M.El-kenawy Nagwan Abdel Samee Mona M.Jamjoom Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Ahmad Taher Azar Nima Khodadadi Reham A.Ghanem Mahmoud Y.Shams 

机构地区：[1]Department of Information Technology,College of Computer and Information Sciences,Princess Nourah bint Abdulrahman University,P.O.Box 84428,Riyadh,11671,Saudi Arabia [2]Department of Communications and Electronics,Delta Higher Institute of Engineering and Technology,Mansoura,35111,Egypt [3]Faculty of Artificial Intelligence,Delta University for Science and Technology,Mansoura,35712,Egypt [4]Department of Computer Sciences,College of Computer and Information Sciences,Princess Nourah bint Abdulrahman University,P.O.Box 84428,Riyadh,11671,Saudi Arabia [5]Computer Engineering and Control Systems Department,Faculty of Engineering,Mansoura University,Mansoura,35516,Egypt [6]Department of Computer Science,Faculty of Computer and Information Sciences,Ain Shams University,Cairo,11566,Egypt [7]Department of Computer Science,College of Computing and Information Technology,Shaqra University,11961,Saudi Arabia [8]Faculty of Computers and Artificial Intelligence,Benha University,Benha,13518,Egypt [9]College of Computer and Information Sciences,Prince Sultan University,Riyadh,11586,Saudi Arabia [10]Department of Civil Engineering,University of Science and Technology,Miami,33101,USA [11]Department of Civil and Environmental Engineering,Florida International University,Miami,USA [12]Oral Biology Department,Faculty of Oral and Dental Medicine,Delta University for Science and Technology,Gamasa,Egypt [13]Faculty of Artificial Intelligence,Kafrelsheikh University,Kafrelsheikh,33511,Egypt

出　　处：《Computers, Materials & Continua》2023年第4期1883-1900,共18页计算机、材料和连续体（英文）

基　　金：Princess Nourah bint Abdulrahman University Researchers Supporting Project Number (PNURSP2022R104),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.

摘　　要：The rapid population growth results in a crucial problem in the early detection of diseases inmedical research.Among all the cancers unveiled,breast cancer is considered the second most severe cancer.Consequently,an exponential rising in death cases incurred by breast cancer is expected due to the rapid population growth and the lack of resources required for performing medical diagnoses.Utilizing recent advances in machine learning could help medical staff in diagnosing diseases as they offer effective,reliable,and rapid responses,which could help in decreasing the death risk.In this paper,we propose a new algorithm for feature selection based on a hybrid between powerful and recently emerged optimizers,namely,guided whale and dipper throated optimizers.The proposed algorithm is evaluated using four publicly available breast cancer datasets.The evaluation results show the effectiveness of the proposed approach from the accuracy and speed perspectives.To prove the superiority of the proposed algorithm,a set of competing feature selection algorithms were incorporated into the conducted experiments.In addition,a group of statistical analysis experiments was conducted to emphasize the superiority and stability of the proposed algorithm.The best-achieved breast cancer prediction average accuracy based on the proposed algorithm is 99.453%.This result is achieved in an average time of 3.6725 s,the best result among all the competing approaches utilized in the experiments.

关 键 词：Medical dataset breast cancer guided whale optimizer dipper throated optimizer feature selection META-HEURISTICS 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]