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

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...

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Veröffentlicht in:Diagnostics (Basel) Jg. 13; H. 12; S. 2038
Hauptverfasser: Alhussan, Amel Ali, Abdelhamid, Abdelaziz A., Towfek, S. K., Ibrahim, Abdelhameed, Eid, Marwa M., Khafaga, Doaa Sami, Saraya, Mohamed S.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland MDPI AG 01.06.2023
MDPI
Schlagworte:
Accuracy
Al-Biruni earth radius optimization
Algorithms
Chronic illnesses
Classification
Datasets
Decision trees
diabetes
dipper throated optimization
Disease
Epidemiology
Feature selection
Forecasts and trends
Gestational diabetes
Hyperglycemia
Insulin resistance
Literature reviews
Machine learning
Mathematical optimization
Medical research
Medicine, Experimental
Neural networks
Optimization algorithms
Optimization techniques
Public health
random forest
Risk factors
Support vector machines
Type 2 diabetes
random forest
machine learning
dipper throated optimization
diabetes
feature selection
Al-Biruni earth radius optimization
ISSN:2075-4418, 2075-4418
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Zusammenfassung: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.
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ISSN:2075-4418
2075-4418
DOI:10.3390/diagnostics13122038