High Performance Implementation of Neural Networks Learning Using Swarm Optimization Algorithms for EEG Classification Based on Brain Wave Data

Bataineh, Ali Al; Jarrah, Amin

doi:10.4018/ijamc.292500

Cited by 9 publications

(6 citation statements)

References 31 publications

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“…Machine Learning (ML) models [9] have been developed in most medical implementations as an encouraging tool to help in taking spontaneous conclusions related to various infections, along with DM, which produces favorable results. With ML algorithms, vast amounts of data are processed by minimizing the effort [10].…”

Section: B Role Of Machine Learning In Diabetes Detectionmentioning

confidence: 99%

Prediction of Micro Vascular and Macro Vascular Complications in Type-2 Diabetic Patients using Machine Learning Techniques

Vamsi¹,

Bataineh²,

Doppala³

2022

IJACSA

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A collection of metabolic conditions known as diabetes mellitus are defined by hyperglycemia brought on by deficiencies in insulin secretion, action, or both. In terms of mortality rate, type-2 diabetes is 20 times higher when compared with type-1. Based on the earlier research, there is still scope to identify different risk levels of type-2 diabetes complications. To achieve this, we have proposed a T2DC machine learning-based prediction system using a decision tree as a base estimator with random forest to identify the severity of T2-DM complications at an early stage. Our proposed model achieved accuracies of 95.43%, 94.62%, 96.25%, 97.55%, and 97.83% for Nephropathy, Neuropathy, Retinopathy, Cardiovascularand Peripheral Vascular complications in T2-DM patients. The proposed model has the potential to improve clinical outcomes by promoting the delivery of early and personalized care to T2-DM patients.

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Section: B Role Of Machine Learning In Diabetes Detectionmentioning

confidence: 99%

Prediction of Micro Vascular and Macro Vascular Complications in Type-2 Diabetic Patients using Machine Learning Techniques

Vamsi¹,

Bataineh²,

Doppala³

2022

IJACSA

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“…al proposed a system [11] that consists of a deep neural network approach for disease prediction with an accuracy of 98.8%. The goal of Al Batainen et al is to create a hybrid framework for the prediction, Authors proposed Grey Woolf and Particle Swarm optimization as machine learning algorithms (GWO-PSO) [12]. The system gives an accuracy of 97.67%.…”

Section: Related Workmentioning

confidence: 99%

“…The combination of GWO and PSO was used for classification. [12] RF, ET 94.41 FCBF, mRMR, LASSO, and Relief feature selection methods were applied on Hungarian and Cleveland databases. [13] LG 87.1…”

Section: Referencesmentioning

confidence: 99%

GA-SLE: A hybrid algorithm for heart disease prediction using feature selection mechanism

Kushwaha

Thirunavukkarasan

2022

Preprint

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Heart disease is the leading cause of death around the globe, killing more people than cancer. A study published in the Journal of the American Heart Association shows that gene variants in our genes can increase our risk of heart failure by as much as 40%. To make matters worse, the number of people with advanced heart failure is expected to rise by 30% by the year 2030. Some of the most common heart disease symptoms include chest pain, shortness of breath, and fatigue. It has been observed that machine learning can provide efficient heart disease prediction over a large amount of data thereby, empowering the patients and health care experts with the knowledge to make more accurate decisions at an appropriate time-bound. The central aspect of our proposed research is to build up a hybrid machine learning classifier using a Genetic Algorithm with a Super learner ensemble (GA-SLE) for the detection of heart disease with increased accuracy. In our study, the proposed system is compared with various machine learning algorithms such as Random Forest (RF), Multilayer Perceptron (MLP), K- Nearest Neighbour (KNN), Extra Tree (ET), Extreme Gradient Boosting (XGB), Super Vector Classifier (SVC), Stochastic Gradient Descent(SGD), AdaBoost, Decision Tree(CART), Gradient Boosting(GBM) using 10-Fold cross-validation and hyperparameter tuning. A regressed comparative analysis of all three approaches has been performed with the help of a table and plot. The proposed GA-SLE outperforms all other ML classifiers, obtaining a prediction accuracy of 99.8% with minimum error loss. As per our findings, the current GA-SLE classifier enables the practitioner to detect and diagnose the sickness in the early stage of the disease.

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“…Machine Learning, or ML, is becoming increasingly popular in the medical field, particularly in diagnostics and treatment management [ 1 , 2 ]. There has been a great deal of research into how ML can improve both the timing and accuracy of diagnosis [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

MLP-PSO Hybrid Algorithm for Heart Disease Prediction

Bataineh

Manacek

2022

JPM

Self Cite

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Background: Machine Learning (ML) is becoming increasingly popular in healthcare, particularly for improving the timing and accuracy of diagnosis. ML can provide disease prediction by analyzing vast amounts of healthcare data, thereby, empowering patients and healthcare providers with information to make informed decisions about disease prevention. Due to the rising cost of treatment, one of the most important topics in clinical data analysis is the prediction and prevention of cardiovascular disease. It is difficult to manually calculate the chances of developing heart disease due to a myriad of contributing factors. Objective: The aim of this paper is to develop and compare various intelligent systems built with ML algorithms for predicting whether a person is likely to develop heart disease using the publicly available Cleveland Heart Disease dataset. This paper describes an alternative multilayer perceptron (MLP) training technique that utilizes a particle swarm optimization (PSO) algorithm for heart disease detection. Methods: The proposed MLP-PSO hybrid algorithm and ten different ML algorithms are used in this study to predict heart disease. Various classification metrics are used to evaluate the performance of the algorithms. Results: The proposed MLP-PSO outperforms all other algorithms, obtaining an accuracy of 84.61%. Conclusions: According to our findings, the current MLP-PSO classifier enables practitioners to diagnose heart disease earlier, more accurately, and more effectively.

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High Performance Implementation of Neural Networks Learning Using Swarm Optimization Algorithms for EEG Classification Based on Brain Wave Data

Cited by 9 publications

References 31 publications

Prediction of Micro Vascular and Macro Vascular Complications in Type-2 Diabetic Patients using Machine Learning Techniques

Prediction of Micro Vascular and Macro Vascular Complications in Type-2 Diabetic Patients using Machine Learning Techniques

GA-SLE: A hybrid algorithm for heart disease prediction using feature selection mechanism

MLP-PSO Hybrid Algorithm for Heart Disease Prediction

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