Scholarlink

<p>Diabetes is a chronic metabolic disorder that, if left untreated, can lead to severe complications and reduced quality of life. Early diagnosis is essential for preventing its adverse effects; however, recent diagnostic methods face challenges such as data imbalance, missing values, and inadequate model accuracy. To address these issues, we propose a novel hybrid framework for diabetes diagnosis, integrating advanced data mining techniques. The proposed methodology combines the Borderline Synthetic Minority Oversampling Technique (BSMOTE) with the Giza Pyramids Construction (GPC) algorithm to generate synthetic samples and mitigate data imbalance. Missing data are imputed using the k-Nearest Neighbors (k-NN), ensuring dataset integrity. For classification, an ensemble approach is employed that integrates k-NN, Support Vector Machines (SVM), Random Forest, and Extreme Learning Machine (ELM) through a majority voting scheme.. The framework is evaluated on the PIMA Indian Diabetes Dataset (PIDD), achieving prediction accuracy ranging from 91% to 93.9%, representing up to a 6% improvement over existing methods. Key contributions include the effective handling of imbalanced and incomplete datasets and the introduction of a robust hybrid classification framework, which demonstrates significant potential for improving early diabetes diagnosis and management.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Improving diabetes diagnosis using metaheuristic-based ensemble classification method

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月11日

查看全文
  • Sara Sokoot,
  • Farsad Zamani-Boroujeni,
  • Fatemeh Davami,
  • Pouya Derakhshan-Barjoei

摘要

Diabetes is a chronic metabolic disorder that, if left untreated, can lead to severe complications and reduced quality of life. Early diagnosis is essential for preventing its adverse effects; however, recent diagnostic methods face challenges such as data imbalance, missing values, and inadequate model accuracy. To address these issues, we propose a novel hybrid framework for diabetes diagnosis, integrating advanced data mining techniques. The proposed methodology combines the Borderline Synthetic Minority Oversampling Technique (BSMOTE) with the Giza Pyramids Construction (GPC) algorithm to generate synthetic samples and mitigate data imbalance. Missing data are imputed using the k-Nearest Neighbors (k-NN), ensuring dataset integrity. For classification, an ensemble approach is employed that integrates k-NN, Support Vector Machines (SVM), Random Forest, and Extreme Learning Machine (ELM) through a majority voting scheme.. The framework is evaluated on the PIMA Indian Diabetes Dataset (PIDD), achieving prediction accuracy ranging from 91% to 93.9%, representing up to a 6% improvement over existing methods. Key contributions include the effective handling of imbalanced and incomplete datasets and the introduction of a robust hybrid classification framework, which demonstrates significant potential for improving early diabetes diagnosis and management.