Diagnosis of diabetes diseases using optimized fuzzy rule set by grey wolf optimization

Shankar, G. Siva; Manikandan, K.

doi:10.1016/j.patrec.2019.06.005

Cited by 45 publications

(12 citation statements)

References 25 publications

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“…Ganji and Abadeh [38] presented FCS-ANTMINER which is a new classification algorithm obtained by combining Ant Colony Optimization (ACO) and Fuzzy logic. They used this classification algorithm for diabetes disease diagnosis.…”

Section: Diabetesmentioning

confidence: 99%

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Recent Advances in Computer-Aided Medical Diagnosis Using Machine Learning Algorithms With Optimization Techniques

et al. 2021

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Artificial intelligence is a spectacular part of computer engineering that has earned a compelling diversion in the field of medical data classification due to its state-of-art algorithmic strength and learning capabilities. Machine Learning is a major sub-domain of artificial intelligence, where it has become one of most promising fields in computer science. In recent years, there is a large spectrum of healthcare and biomedical data has been growing intensely. Due to the huge labeled or unlabeled data, it is important to have a compact and robust machine learning solution for classification. Several optimizers have been deployed to improve the inclusive performance of machine learning models. The classification of machine learning models depend on several factors. This comprehensive review paper aims to insight the current stage of optimized machine learning success on medical data classification. Increasing number of unstructured medical data has been utilizing in machine learning algorithms to predict intuitions. But it is difficult to inherent immense intuition from those data. So machine learning researchers have utilized stateof-art optimizers and novel feature selection techniques to overcome and emend the performance accuracy. We have highlighted some recent literatures, which exhibits the robust impact of optimizers and feature selection on machine learning techniques on medical data characterization. On the other hand, a cleancut introduction on machine learning and theoretical outlook of widely utilized optimization techniques like genetic algorithm, gray wolf optimization, and particle swarm optimization are discussed for initial understanding to the optimization techniques.

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Section: Diabetesmentioning

confidence: 99%

“…Which leads to high risk of death. Type -2 diabetes leads to high risk and effects the insulin reproduction in the human body [38]. Diabetes can regulate by the certain lifestyle changes, desired drugs and In next few years, diabetes will cause 25% expanded death rate globally.…”

Section: Introductionmentioning

confidence: 99%

Recent Advances in Computer-Aided Medical Diagnosis Using Machine Learning Algorithms With Optimization Techniques

et al. 2021

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“…Diabetes is a prevalent metabolic disease that reduces life expectancy. The prevalence of diabetes is projected to increase from 4% in 1995 to 4.5% in 2035, and by 2045, the number of people suffering from diabetes is estimated to reach 629 million worldwide, according to the World Health Organization (WHO) [1,2]. Type 2 diabetes mellitus (T2DM), which accounts for more than 90% of diabetes cases, is one of the four priority noncommunicable diseases identified by the WHO [3].…”

Section: Introductionmentioning

confidence: 99%

“…A high HbA1c level not only leads to the deterioration of T2DM but also leads to many fatal health complications, such as kidney failure, blindness, heart disease, etc. [2]. HbA1c is the most valuable index to judge the state of blood sugar control and the progression of T2DM.…”

Section: Introductionmentioning

confidence: 99%

“…They closely monitor the level of HbA1c in patients with T2DM and adjust the treatment plan accordingly to provide the appropriate clinical intervention that will stabilize its levels near the optimal value. In addition, in 2009, the International Expert Committee recommended the use of HbA1c to diagnose T2DM and prediabetes [2]. In 2010, the American Diabetes Association (ADA) endorsed the recommendations of the Expert Committee on the diagnosis of T2DM and recommended HbA1c as the preferred method [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

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Sequential Prediction of Glycosylated Hemoglobin Based on Long Short-Term Memory with Self-Attention Mechanism

Wang¹,

Gong²,

Zhu³

et al. 2020

IJCIS

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Type 2 diabetes mellitus (T2DM) has been identified as one of the most challenging chronic diseases to manage. In recent years, the incidence of T2DM has increased, which has seriously endangered people's health and life quality. Glycosylated hemoglobin (HbA1c) is the gold standard clinical indicator of the progression of T2DM. An accurate prediction of HbA1c levels not only helps medical workers improve the accuracy of clinical decision-making but also helps patients to better understand the clinical progression of T2DM and conduct self-management to achieve the goal of controlling the progression of T2DM. Therefore, we introduced the long short-term memory (LSTM) neural network to predict patients' HbA1c levels using time sequential data from electronic medical records (EMRs). We added the self-attention mechanism based on the traditional LSTM to capture the long-term interdependence of feature elements and which ensure that the memory was more profound and effective, and used the gradient search technology to minimize the mean square error of the predicted value of the network and the real value. LSTM with the self-attention mechanism performed better than the traditional deep learning sequence prediction method. Our research provides a good reference for the application of deep learning in the field of medical health management.

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A Comprehensive Study on the Application of Grey Wolf Optimization for Microarray Data

Sucharita

Sahu

Swarnkar

2021

Data Analytics in Bioinformatics

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Diagnosis of diabetes diseases using optimized fuzzy rule set by grey wolf optimization

Cited by 45 publications

References 25 publications

Recent Advances in Computer-Aided Medical Diagnosis Using Machine Learning Algorithms With Optimization Techniques

Recent Advances in Computer-Aided Medical Diagnosis Using Machine Learning Algorithms With Optimization Techniques

Sequential Prediction of Glycosylated Hemoglobin Based on Long Short-Term Memory with Self-Attention Mechanism

A Comprehensive Study on the Application of Grey Wolf Optimization for Microarray Data

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