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 

作者机构：Department of Information TechnologyCollege of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and ElectronicsDelta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial IntelligenceDelta University for Science and TechnologyMansoura35712Egypt Department of Computer SciencesCollege of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Computer Engineering and Control Systems DepartmentFaculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer ScienceFaculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer ScienceCollege of Computing and Information TechnologyShaqra University11961Saudi Arabia Faculty of Computers and Artificial IntelligenceBenha UniversityBenha13518Egypt College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Civil EngineeringUniversity of Science and TechnologyMiami33101USA Department of Civil and Environmental EngineeringFlorida International UniversityMiamiUSA Oral Biology DepartmentFaculty of Oral and Dental MedicineDelta University for Science and TechnologyGamasaEgypt Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikh33511Egypt 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2023年第75卷第4期

页      面：1883-1900页

核心收录：

学科分类：08[工学] 0835[工学-软件工程] 081202[工学-计算机软件与理论] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

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

主　　题：Medical dataset breast cancer guided whale optimizer dipper throated optimizer feature selection meta-heuristics 

摘      要：The rapid population growth results in a crucial problem in the early detection of diseases inmedical *** all the cancers unveiled,breast cancer is considered the second most severe ***,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 *** 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 *** 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 *** proposed algorithm is evaluated using four publicly available breast cancer *** evaluation results show the effectiveness of the proposed approach from the accuracy and speed *** prove the superiority of the proposed algorithm,a set of competing feature selection algorithms were incorporated into the conducted *** addition,a group of statistical analysis experiments was conducted to emphasize the superiority and stability of the proposed *** 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.