Using Genetic Algorithm and ELM Neural Networks for Feature Extraction and Classification of Type 2-Diabetes Mellitus

Alharbi, Abir; Alghahtani, Munirah

doi:10.1080/08839514.2018.1560545

Cited by 48 publications

(18 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The KELM-HAFPSO model proposed has been assessed for Accuracy, Sensitivity, Specificity, MCC, and KS by means of 5-fold crossvalidation on the basis of two related datasets. Comparative Analysis was conducted between the proposed KELM-HAFSO with the other five competitive methods namely ELM-GA [43], Decision Tree C4.5-PSO [27], k-NN [22], MLP [21], LR [17], SVM [38] and NB [16].…”

Section: Discussionmentioning

confidence: 99%

Risk Assessment of Type 2 Diabetes Mellitus Prediction using an Improved Combination of NELM-PSO

Reddy

Mahesh

2018

ICST Transactions on Scalable Information Systems

View full text Add to dashboard Cite

Risk iAssessment of iDiabetes Type-II is crucial in ipreventing it and ireducing the risk of various comorbidities. There are many iexisting machine ilearning models for predicting iType-II diabetics in ishort term future or in unspecified future. But obtaining a model having optimal performance and predicting idiabetes risk in long term future are the main problems. iThese problems are ihandled in this work by iproposing ia stacking based integrated KELM imodel to predict the irisk of diabetes Type-II for a person within five years after assessment. The Pima Indian Diabetic Dataset (PIDD) and a Diabetic Research Center dataset are used in this study. iA Min-Max normalization is iused to pre-process ithe noisy idatasets. The HAFPSO ialgorithm used in ithis work iexplores the best combination of Base ilearners by increasing the iClassification Accuracy (CA) iand decreasingi the kernel icomplexity of the ioptimal learners. iFinally, the model is iintegrated by utilizing the iKELM as a meta-classifier that icombines the ipredictions of the twenty Base Learners. The iproposed imethod is assessed iwith different imeasures such as accuracy, isensitivity, ispecificity, Mathews iCorrelation iCoefficient, and Kappa Statistics. iThe proposed KELM-HAFPSO iapproach has got ibetter values iof the considered metrics confirming its effectiveness in identifying type-II diabetes. iThe proposed method helps the clinicians to predict the ipatients who iare at a ihigh risk of Type-II diabetes iin the ifuture with the ihighest iaccuracy of 98.5%. The results iobtained show that ithe KELM-HAFPSO iapproach is a ipromising new itool for identifying type-II diabetes.

show abstract

Section: Discussionmentioning

confidence: 99%

Risk Assessment of Type 2 Diabetes Mellitus Prediction using an Improved Combination of NELM-PSO

Reddy

Mahesh

2018

ICST Transactions on Scalable Information Systems

View full text Add to dashboard Cite

show abstract

“…The genetic algorithm is a well-known optimization technique that may be categorized as an evolutionary method using biological process [ 19 ]. Genetic algorithms have been demonstrated to be reliable and robust in many medical applications [ 20 - 22 ]. A genetic algorithm that included chromosome reproduction, crossover, and mutation heuristic processes was implemented.…”

Section: Methodsmentioning

confidence: 99%

Neural Network–Based Clinical Prediction System for Identifying the Clinical Effects of Saffron (Crocus sativus L) Supplement Therapy on Allergic Asthma: Model Evaluation Study

et al. 2020

View full text Add to dashboard Cite

Background Asthma is commonly associated with chronic airway inflammation and is the underlying cause of over a million deaths each year. Crocus sativus L, commonly known as saffron, when used in the form of traditional medicines, has demonstrated anti-inflammatory effects which may be beneficial to individuals with asthma. Objective The objective of this study was to develop a clinical prediction system using an artificial neural network to detect the effects of C sativus L supplements on patients with allergic asthma. Methods A genetic algorithm–modified neural network predictor system was developed to detect the level of effectiveness of C sativus L using features extracted from the clinical, immunologic, hematologic, and demographic information of patients with asthma. The study included data from men (n=40) and women (n=40) individuals with mild or moderate allergic asthma from 18 to 65 years of age. The aim of the model was to estimate and predict the level of effect of C sativus L supplements on each asthma risk factor and to predict the level of alleviation in patients with asthma. A genetic algorithm was used to extract input features for the clinical prediction system to improve its predictive performance. Moreover, an optimization model was developed for the artificial neural network component that classifies the patients with asthma using C sativus L supplement therapy. Results The best overall performance of the clinical prediction system was an accuracy greater than 99% for training and testing data. The genetic algorithm–modified neural network predicted the level of effect with high accuracy for anti–heat shock protein (anti-HSP), high sensitivity C-reactive protein (hs-CRP), forced expiratory volume in the first second of expiration (FEV1), forced vital capacity (FVC), the ratio of FEV1/FVC, and forced expiratory flow (FEF25%-75%) for testing data (anti-HSP: 96.5%; hs-CRP: 98.9%; FEV1: 98.1%; FVC: 97.5%; FEV1/FVC ratio: 97%; and FEF25%-75%: 96.7%, respectively). Conclusions The clinical prediction system developed in this study was effective in predicting the effect of C sativus L supplements on patients with allergic asthma. This clinical prediction system may help clinicians to identify early on which clinical factors in asthma will improve over the course of treatment and, in doing so, help clinicians to develop effective treatment plans for patients with asthma.

show abstract

“…the important parameter values (input weight value and hidden layer threshold) in ELM, instead of the traditional BP parameter optimization algorithm, to obtain a more compact network structure and improve the accuracy of data classification. Alharbi et al [21] used the genetic algorithm of integer coding and combined with the ELM classifier to study gene selection and cancer classification. GA algorithm is used for feature selection, redundant features are removed, and the most important features are selected as the input of ELM classifier.…”

Section: Related Workmentioning

confidence: 99%

Improved Crow Search Algorithm Optimized Extreme Learning Machine Based on Classification Algorithm and Application

et al. 2021

View full text Add to dashboard Cite

In view of the problems of the connection weights and thresholds of the extreme learning machine are randomly generated before training and remain unchanged during the training process, the number of hidden layer nodes is pre-allocated, and the hidden layer parameters are randomly selected. Too many hidden layer nodes not only make the network more complex but also reduce the generalization ability of the algorithm. Aiming at this problem, an improved crow search algorithm is proposed to optimize the extreme learning machine. Based on the analysis of the limitations of the original crow search algorithm, a particle swarm algorithm search strategy is proposed to enhance the global search capability. In the latter part of the algorithm iteration, Gaussian function is added, and the penalty coefficient of the function is used for local disturbance, gradually reducing the amplitude of the search trajectory, and then adaptively adjusting the parameters to avoid being attracted by local extremum. Finally, the improved crow search algorithm is used to optimize the hidden layer neurons and connection weights of the extreme learning machine neural network, so as to obtain accurate prediction results. Through function fitting, regression data set fitting and classification data set for classification experiment verification, the proposed algorithm has higher training speed and efficiency. At the same time, this method is not only significantly higher than the traditional ELM method, but also obtains a more compact network structure, which is an effective neural network optimization algorithm.

show abstract

Using Genetic Algorithm and ELM Neural Networks for Feature Extraction and Classification of Type 2-Diabetes Mellitus

Cited by 48 publications

References 21 publications

Risk Assessment of Type 2 Diabetes Mellitus Prediction using an Improved Combination of NELM-PSO

Risk Assessment of Type 2 Diabetes Mellitus Prediction using an Improved Combination of NELM-PSO

Neural Network–Based Clinical Prediction System for Identifying the Clinical Effects of Saffron (Crocus sativus L) Supplement Therapy on Allergic Asthma: Model Evaluation Study

Improved Crow Search Algorithm Optimized Extreme Learning Machine Based on Classification Algorithm and Application

Contact Info

Product

Resources

About