Classification of Diabetes Using Feature Selection and Hybrid Al-Biruni Earth Radius and Dipper Throated Optimization

被引:10
|
作者
Alhussan, Amel Ali [1 ]
Abdelhamid, Abdelaziz A. [2 ,3 ]
Towfek, S. K. [4 ,5 ]
Ibrahim, Abdelhameed [6 ]
Eid, Marwa M. [7 ]
Khafaga, Doaa Sami [1 ]
Saraya, Mohamed S. [6 ]
机构
[1] Princess Nourah bint Abdulrahman Univ, Coll Comp & Informat Sci, Dept Comp Sci, POB 84428, Riyadh 11671, Saudi Arabia
[2] Shaqra Univ, Coll Comp & Informat Technol, Dept Comp Sci, Shaqra 11961, Saudi Arabia
[3] Ain Shams Univ, Fac Comp & Informat Sci, Dept Comp Sci, Cairo 11566, Egypt
[4] Comp Sci & Intelligent Syst Res Ctr, Blacksburg, VA 24060 USA
[5] Delta Higher Inst Engn & Technol, Dept Commun & Elect, Mansoura 35111, Egypt
[6] Mansoura Univ, Fac Engn, Comp Engn & Control Syst Dept, Mansoura 35516, Egypt
[7] Delta Univ Sci & Technol, Fac Artificial Intelligence, Mansoura 11152, Egypt
关键词
diabetes; machine learning; feature selection; Al-Biruni earth radius optimization; dipper throated optimization; random forest;
D O I
10.3390/diagnostics13122038
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Introduction: In public health, machine learning algorithms have been used to predict or diagnose chronic epidemiological disorders such as diabetes mellitus, which has reached epidemic proportions due to its widespread occurrence around the world. Diabetes is just one of several diseases for which machine learning techniques can be used in the diagnosis, prognosis, and assessment procedures. Methodology: In this paper, we propose a new approach for boosting the classification of diabetes based on a new metaheuristic optimization algorithm. The proposed approach proposes a new feature selection algorithm based on a dynamic Al-Biruni earth radius and dipper-throated optimization algorithm (DBERDTO). The selected features are then classified using a random forest classifier with its parameters optimized using the proposed DBERDTO. Results: The proposed methodology is evaluated and compared with recent optimization methods and machine learning models to prove its efficiency and superiority. The overall accuracy of diabetes classification achieved by the proposed approach is 98.6%. On the other hand, statistical tests have been conducted to assess the significance and the statistical difference of the proposed approach based on the analysis of variance (ANOVA) and Wilcoxon signed-rank tests. Conclusions: The results of these tests confirmed the superiority of the proposed approach compared to the other classification and optimization methods.
引用
收藏
页数:40
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