Modelling, simulation, and optimization of diabetes type II prediction using deep extreme learning machine

Rehman, Abdur; Athar, Atifa; Khan, Muhammad Adnan; Abbas, Sagheer; Fatima, Areej; Rahman, Atta; Saeed, Anwaar

doi:10.3233/ais-200554

Cited by 55 publications

(45 citation statements)

References 30 publications

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“…It is one of the most widespread and lasting diseases that affect numerous difficulties if not treated and anonymous [2]. The individual organ identified as the 'pancreas' is liable to produce hormones termed insulin, liable to regulate the body's glucose levels [3]. The causes of diabetes include obesity, improper nutrition, heredity, absence of physical activity, and many more [3].…”

Section: Introductionmentioning

confidence: 99%

“…The individual organ identified as the 'pancreas' is liable to produce hormones termed insulin, liable to regulate the body's glucose levels [3]. The causes of diabetes include obesity, improper nutrition, heredity, absence of physical activity, and many more [3]. If it delays the treatment and diagnosis of diabetes or continues anonymous and not treated, it can commence to complex difficulties: blindness, heart failure, kidney malfunction, or many more relevant or irrelevant problems [2], [3].…”

Section: Introductionmentioning

confidence: 99%

“…The causes of diabetes include obesity, improper nutrition, heredity, absence of physical activity, and many more [3]. If it delays the treatment and diagnosis of diabetes or continues anonymous and not treated, it can commence to complex difficulties: blindness, heart failure, kidney malfunction, or many more relevant or irrelevant problems [2], [3]. As living standards and lifestyles are continuously developing, it is becoming overwhelmingly common [3].…”

Section: Introductionmentioning

confidence: 99%

“…If it delays the treatment and diagnosis of diabetes or continues anonymous and not treated, it can commence to complex difficulties: blindness, heart failure, kidney malfunction, or many more relevant or irrelevant problems [2], [3]. As living standards and lifestyles are continuously developing, it is becoming overwhelmingly common [3]. While in the case of HCC, it is the fourth most regular reason for cancer-related mortality in the world [4].…”

Section: Introductionmentioning

confidence: 99%

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Multi-Tier Ensemble Learning Model With Neighborhood Component Analysis to Predict Health Diseases

Dhar¹,

Ayele

2021

IEEE Access

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Various well-known health diseases affect millions of people worldwide. Sometimes in the early stage, the clinicians may not recognize several clinical symptoms due to lack of their symptoms reflection or anything else. Thus, such diseases are not easier to identify. There may have chances to grow these illnesses and affected millions of people worldwide. The risk factor of such diseases severity can be lessened, notably whenever an accurate early prediction is possible. This study presents an innovative multi-tier weighted ensemble learning model (MTWEL) for predicting several diseases such as diabetes and hepatocellular carcinoma (HCC) and, therefore, reduces such above-said problems from the sufferers and lessens the chances of mortality. In the MTWEL model, we have utilized two lists of base classifiers in which six various machine learning (ML) classifiers are assigned in each list to develop two weighted ensemble learning (EL) models and combine them to form the proposed model by employing a weighted voting approach. In the MTWEL model, the parameters of all employed classifiers are tuned through the genetic algorithm-enabled hyperparameter optimization technique to form the optimized base models. The weight of each chosen optimized base model and generated EL model(s) is calculated using Matthews correlation coefficient value with the optimized weight value. In this study, neighborhood component analysis is employed to reduce the dimension of the given input dataset. The suggested model's experimental outcomes are conducted on two real-world datasets to exhibit its performance. The suggested approach receives the best result in AUC values: 1.0 and 1.0, F1-score values: 0.9957 and 0.9947, and accuracy values: 0.9952 and 0.9929. Such outcomes in the form of performance exhibit that the proposed model is the best-suited model to predict several diseases than other techniques, and hence it helps clinicians make accurate decisions.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-Tier Ensemble Learning Model With Neighborhood Component Analysis to Predict Health Diseases

Dhar¹,

Ayele

2021

IEEE Access

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“…Type I diabetes and type 2 diabetics are the common forms of diabetes. In type I, the immune system initiated a misguided attack and destroyed the pancreas beta cells (Rehman et al, 2020). Approximately 5% of people with diagnosed diabetes are affected and it develops mostly during childhood.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of Machine Learning Approaches in Diabetes Prediction

Parameswari¹

2020

Biosci. Biotech. Res. Comm

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ML-Based Prediction of Ideal Discipline for UG Students: A Sustainable Educational Perspective

Khan,

Aljebali,

Youldash

et al. 2024

Technical and Vocational Education and Training: Issues, Concerns and Prospects

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Modelling, simulation, and optimization of diabetes type II prediction using deep extreme learning machine

Cited by 55 publications

References 30 publications

Multi-Tier Ensemble Learning Model With Neighborhood Component Analysis to Predict Health Diseases

Multi-Tier Ensemble Learning Model With Neighborhood Component Analysis to Predict Health Diseases

Comparative Study of Machine Learning Approaches in Diabetes Prediction

ML-Based Prediction of Ideal Discipline for UG Students: A Sustainable Educational Perspective

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