Using fuzzy ant colony optimization for diagnosis of diabetes disease

Ganji, Mostafa Fathi; Abadeh, Mohammad Saniee

doi:10.1109/iraniancee.2010.5507019

Cited by 30 publications

(2 citation statements)

References 10 publications

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“…The rule-based system has a 74% prediction accuracy. Mostafa et al [28] present a Recursive Rule Extraction (Re-Rx) framework to generate decision rules, achieving 83.8% accuracy. In [6], a two-stage hybrid model was presented for decision rule extraction and classification.…”

Section: Related Researchmentioning

confidence: 99%

A Fusion-Based Machine Learning Approach for the Prediction of the Onset of Diabetes

et al. 2021

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A growing portfolio of research has been reported on the use of machine learning-based architectures and models in the domain of healthcare. The development of data-driven applications and services for the diagnosis and classification of key illness conditions is challenging owing to issues of low volume, low-quality contextual data for the training, and validation of algorithms, which, in turn, compromises the accuracy of the resultant models. Here, a fusion machine learning approach is presented reporting an improvement in the accuracy of the identification of diabetes and the prediction of the onset of critical events for patients with diabetes (PwD). Globally, the cost of treating diabetes, a prevalent chronic illness condition characterized by high levels of sugar in the bloodstream over long periods, is placing severe demands on health providers and the proposed solution has the potential to support an increase in the rates of survival of PwD through informing on the optimum treatment on an individual patient basis. At the core of the proposed architecture is a fusion of machine learning classifiers (Support Vector Machine and Artificial Neural Network). Results indicate a classification accuracy of 94.67%, exceeding the performance of reported machine learning models for diabetes by ~1.8% over the best reported to date.

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Section: Related Researchmentioning

confidence: 99%

A Fusion-Based Machine Learning Approach for the Prediction of the Onset of Diabetes

et al. 2021

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“…Diabetes can be controlled with the help of insulin injections, a controlled diet (changing eating habits) and exercise programs, but no whole cure is available. Diabetes leads to many other disease such as blindness, blood pressure, heart disease, kidney disease and nerve damage [15]. Main 3 diabetes signs are:-Increased need to urinate (Polyuria), Increased hunger (Polyphagia), Increased thirst (Polydipsia).…”

Section: Introductionmentioning

confidence: 99%

GA_MLP NN: A Hybrid Intelligent System for Diabetes Disease Diagnosis

Choubey¹,

Paul²

2016

IJISA

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Abstract-Diabetes is a condition in which the amount of sugar in the blood is higher than normal. Classification systems have been widely used in medical domain to explore patient's data and extract a predictive model or set of rules. The prime objective of this research work is to facilitate a better diagnosis (classification) of diabetes disease. There are already several methodology which have been implemented on classification for the diabetes disease. The proposed methodology implemented work in 2 stages: (a) In the first stage Genetic Algorithm (GA) has been used as a feature selection on Pima Indian Diabetes Dataset. (b) In the second stage, Multilayer Perceptron Neural Network (MLP NN) has been used for the classification on the selected feature. GA is noted to reduce not only the cost and computation time of the diagnostic process, but the proposed approach also improved the accuracy of classification. The experimental results obtained classification accuracy (79.1304%) and ROC (0.842) show that GA and MLP NN can be successfully used for the diagnosing of diabetes disease.

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