Volume 22, Issue 1 (10-2024)
sjsph 2024, 22(1): 41-54 |
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Ahangarani M, Tarokh M J. Diagnosis of Diabetes Using Machine Learning and Evolutionary Algorithms. sjsph 2024; 22 (1) :41-54
URL: http://sjsph.tums.ac.ir/article-1-6306-en.html
URL: http://sjsph.tums.ac.ir/article-1-6306-en.html
1- , mjtarokh@kntu.ac.ir
Abstract: (212 Views)
Background and Aim: In recent years, machine learning and evolutionary algorithms have drawn the attention of researchers and specialists in various fields, especially in healthcare, due to their practical applications in processing large datasets to provide valuable insights. Considering the increasing prevalence of diabetes and its rapid and accurate diagnosis being one of the most critical issues in medicine, significant concerns are faced by global communities worldwide. The present study was conducted with the aim of creating a diagnostic model based on evolutionary algorithms and machine learning to diagnose diabetes.
Materials and Methods: This research based on the Indian Pima diabetes dataset presents a framework based on intelligent diabetes diagnosis. The proposed method consists of two main stages. The first stage involves a classification approach using K-nearest neighbors and random forest algorithms. The second stage includes a combined feature selection and classification approach to enhance the results of the first stage, utilizing grey wolf optimization, whale optimization, and particle swarm optimization algorithms for feature selection. Comparative analysis among different approaches is conducted through evaluation metrics such as accuracy, precision, recall, and F1-score.
Results: After comparative comparisons among the proposed models, the random forest model based on the grey wolf optimization was selected and introduced as the final model with a prediction accuracy of 81.38%.
Conclusion: The findings of this research indicate that the use of evolutionary algorithms alongside machine learning models can often enhance the efficiency and accuracy of diabetes diagnosis and its associated complications.
Materials and Methods: This research based on the Indian Pima diabetes dataset presents a framework based on intelligent diabetes diagnosis. The proposed method consists of two main stages. The first stage involves a classification approach using K-nearest neighbors and random forest algorithms. The second stage includes a combined feature selection and classification approach to enhance the results of the first stage, utilizing grey wolf optimization, whale optimization, and particle swarm optimization algorithms for feature selection. Comparative analysis among different approaches is conducted through evaluation metrics such as accuracy, precision, recall, and F1-score.
Results: After comparative comparisons among the proposed models, the random forest model based on the grey wolf optimization was selected and introduced as the final model with a prediction accuracy of 81.38%.
Conclusion: The findings of this research indicate that the use of evolutionary algorithms alongside machine learning models can often enhance the efficiency and accuracy of diabetes diagnosis and its associated complications.
Type of Study: Research |
Subject:
Public Health
Received: 2024/10/16 | Accepted: 2024/10/1 | Published: 2024/10/1
Received: 2024/10/16 | Accepted: 2024/10/1 | Published: 2024/10/1
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