Improving Thyroid Disorder Diagnosis via Ensemble Stacking and Bidirectional Feature Selection

作     者：Muhammad Armghan Latif Zohaib Mushtaq Saad Arif Sara Rehman Muhammad Farrukh Qureshi Nagwan Abdel Samee Maali Alabdulhafith Yeong Hyeon Gu Mohammed A.Al-masni 

作者机构：Department of Computer and Information SystemCleveland State UniversityOhio44115USA Department of ElectricalElectronics and Computer SystemsCollege of Engineering and TechnologyUniversity of SargodhaSargodha40100Pakistan Department of Mechanical EngineeringCollege of EngineeringKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Biomedical EngineeringRiphah International UniversityIslamabad44000Pakistan Department of Electrical EngineeringRiphah International UniversityIslamabad44000Pakistan Department of Information TechnologyCollege of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Artificial IntelligenceCollege of Software and Convergence TechnologySejong UniversitySeoul05006Korea 

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

年 卷 期：2024年第78卷第3期

页      面：4225-4241页

核心收录：

学科分类：1002[医学-临床医学] 100201[医学-内科学(含：心血管病、血液病、呼吸系病、消化系病、内分泌与代谢病、肾病、风湿病、传染病)] 10[医学] 

基　　金：supported by the Institute of Information&communications Technology Planning&Evaluation(IITP)grant Funded by the Korean government(MSIT)(2021-0-00755,Dark Data Analysis Technology for Data Scale and Accuracy Improvement) This research was funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2024R407) Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia 

主　　题：Ensemble learning random forests boosting dimensionality reduction machine learning smart healthcare computer aided diagnosis 

摘      要：Thyroid disorders represent a significant global health challenge with hypothyroidism and hyperthyroidism as two common conditions arising from dysfunction in the thyroid *** and timely diagnosis of these disorders is crucial for effective treatment and patient *** research introduces a comprehensive approach to improve the accuracy of thyroid disorder diagnosis through the integration of ensemble stacking and advanced feature selection *** forward feature selection,sequential backward feature elimination,and bidirectional feature elimination are investigated in this *** ensemble learning,random forest,adaptive boosting,and bagging classifiers are *** effectiveness of these techniques is evaluated using two different datasets obtained from the University of California Irvine-Machine Learning Repository,both of which undergo preprocessing steps,including outlier removal,addressing missing data,data cleansing,and feature *** experimentation demonstrates the remarkable success of proposed ensemble stacking and bidirectional feature elimination achieving 100%and 99.86%accuracy in identifying hyperthyroidism and hypothyroidism,*** enhancing detection accuracy,the ensemble stacking model also demonstrated a streamlined computational complexity which is pivotal for practical medical *** significantly outperformed existing studies with similar objectives underscoring the viability and effectiveness of the proposed *** research offers an innovative perspective and sets the platform for improved thyroid disorder diagnosis with broader implications for healthcare and patient well-being.