Bio-Inspired Machine Learning Approach to Type 2 Diabetes Detection

Type 2 diabetes is a common life-changing disease that has been growing rapidly in recent years. According to the World Health Organization, approximately 90% of patients with diabetes worldwide have type 2 diabetes. Although there is no permanent cure for type 2 diabetes, this disease needs to be d...

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Vydáno v:Symmetry (Basel) Ročník 15; číslo 3; s. 764
Hlavní autoři: Al-Tawil, Marwan, Mahafzah, Basel A., Al Tawil, Arar, Aljarah, Ibrahim
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.03.2023
Témata:
Accuracy
Algorithms
Analysis
Artificial intelligence
Asymmetry
Cancer
Chronic diseases
Chronic illnesses
Classification
Data mining
Datasets
Decision trees
Deep learning
Diabetes
Diabetics
Feature selection
Genetic algorithms
Heuristic methods
Machine learning
Optimization
Performance enhancement
Regression analysis
Support vector machines
Type 2 diabetes
ISSN:2073-8994, 2073-8994
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Shrnutí:Type 2 diabetes is a common life-changing disease that has been growing rapidly in recent years. According to the World Health Organization, approximately 90% of patients with diabetes worldwide have type 2 diabetes. Although there is no permanent cure for type 2 diabetes, this disease needs to be detected at an early stage to provide prognostic support to allied health professionals and develop an effective prevention plan. This can be accomplished by analyzing medical datasets using data mining and machine-learning techniques. Due to their efficiency, metaheuristic algorithms are now utilized in medical datasets for detecting chronic diseases, with better results than traditional methods. The main goal is to improve the performance of the existing approaches for the detection of type 2 diabetes. A bio-inspired metaheuristic algorithm called cuttlefish was used to select the essential features in the medical data preprocessing stage. The performance of the proposed approach was compared to that of a well-known bio-inspired metaheuristic feature selection algorithm called the genetic algorithm. The features selected from the cuttlefish and genetic algorithms were used with different classifiers. The implementation was applied to two datasets: the Pima Indian diabetes dataset and the hospital Frankfurt diabetes dataset; generally, these datasets are asymmetry, but some of the features in these datasets are close to symmetry. The results show that the cuttlefish algorithm has better accuracy rates, particularly when the number of instances in the dataset increases.
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ISSN:2073-8994
2073-8994
DOI:10.3390/sym15030764