FPGA Design and Hardware Implementation of Heart Disease Diagnosis System Based on NVG-RAM Classifier

Hasan, Tabreer T.; Jasim, Manal H.; Hashim, Ivan A.

doi:10.1109/scee.2018.8684125

Cited by 18 publications

(11 citation statements)

References 6 publications

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“…The presentation of this classifier using a CD was examined. Besides, the designed algorithm is viewed as a decent one by the researcher in [13] to diagnosis multiclass sickness of heart that shows up 88% of accuracy.…”

Section: Related Workmentioning

confidence: 99%

Heart Disease Prediction Using Glowworm Swarm Optimization and Support Vector Machine Classifier

Sreedevi

2022

International Journal of Biology and Biomedical Engineering

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At present, health disorder is growing day by way of the day due to existence lifestyle, hereditary. Particularly, heart disease has ended up greater frequent these days. Heart disorder prognosis technique is very quintessential and integral trouble for the patient's health. Besides, it will help out to limit disorder to a larger distinctive level. The role of using strategy like machine learning and algorithm such as heart disease diagnosis using Data Mining(DM) techniques is very significant. In the previous system, the Fuzzy Extreme Learning Machine (FELM) was proposed to predict heart disease, ensuring an accurate and timely diagnosis. However, it only achieves 87.14 % of accuracy. To improve the classification accuracy, the proposed system designed an Improved Step Adjustment based Glowworm Swarm Optimization Algorithm with Weighted Feature based Support Vector Machine (ISAGSO-WFSVM) for Heart disease diagnosis. This proposed venture utilizes the dataset of heart disease for input. Using the Improved Step Adjustment based Glowworm Swarm Optimization Algorithm (ISAGSO) to enhance the true positive rate, optimal features are then selected. Finally, with the aid of the Weighted Feature based Support Vector Machine (WFSVM) classifier, classification is carried out relying selected features. In the proposed method, better performance obtained and that is validated through the experimental results in terms of precision, accuracy, recall and f-measures

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Section: Related Workmentioning

confidence: 99%

Heart Disease Prediction Using Glowworm Swarm Optimization and Support Vector Machine Classifier

Sreedevi

2022

International Journal of Biology and Biomedical Engineering

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“…Therefore, many feature extraction types are used to reduce the raw data dimensions and produce new vectors that will enter the classification stage instead of the raw data. So, the new vectors must contain all the required information to obtain fast training [12,13].…”

Section: Feature Extractionmentioning

confidence: 99%

EMG Signals Classification of Wide Range Motion Signals for Prosthetic Hand Control

Mahmood¹

2021

IJIES

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The recent revolution in the biomedical field carried out the researchers to work on the prosthetic technique because it reflects the amputee's need. Therefore, the electromyography (EMG) signals generated by muscle contractions are used to implement the prosthetic human body parts. This paper presents a pattern recognition system based on two EMG data; the first EMG data represents the general body movements collected from biceps and triceps muscles for six different motions, i.e., bowing, clapping, handshaking, hugging, jumping, and running. The Root Mean Square, Difference Absolute Standard Deviation Value, and Principal Component Analysis extract the raw signal data features and enhance classification accuracy. The machine learning method is applied, i.e., Support Vector Machine (SVM) and K-Nearest Neighbours (KNN) are used for data classification; the results show high accuracy reached 94.8% and 98.9%, respectively. Whereas, the second EMG data is selected to be more specific in hand movements, i.e., cylindrical, spherical, palmar, lateral, hook, and tip motions, because these significant motions are the first step implementing any prosthetic hand. Consequently, the mean, Standard Deviation Value, and Principal Component Analysis extract the raw signal feature. Meanwhile, the same algorithm used in the first data classification is also used to classify the second data because it shows high accuracy and good performance. SVM algorithm is used to classify the data and achieved high training accuracy, reaching 89%. The high training accuracy for different hand movements is considered an essential step toward implementing human prosthetic parts to help the people who suffer from an amputee.

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“…Currently, a person's workload has significantly increased as a result of more work. There is a great likelihood that the person would get heart disease as a result of this terrible situation, which cannot be avoided [1][2][3]. Heart diseases are brought on by a reduction in the amount of blood circulating to the brain, heart, lungs, and other vital organs.…”

Section: Introductionmentioning

confidence: 99%

EHMFFL: Ensemble Heuristic-Metaheuristic Feature Fusion Learning Algorithm for Heart Disease Diagnosis

Shokouhifar,

Hasanvand,

Moharamkhani

et al. 2023

Preprint

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Heart disease is a global health concern of paramount importance, causing a significant number of fatalities and disabilities. Precise and timely diagnosis of heart disease is pivotal in pre-venting adverse outcomes and improving patient well-being, thereby creating a growing demand for intelligent approaches to predict heart disease effectively. This paper introduces an Ensemble Heuristic-Metaheuristic Feature Fusion Learning (EHMFFL) algorithm for heart disease diagnosis. Within the EHMFFL algorithm, a diverse ensemble learning model is crafted, featuring different feature subsets for each heterogeneous base learner, including support vector machine, K-nearest neighbors, logistic regression, random forest, naive bayes, decision tree, and XGBoost. The primary objective is to identify the most pertinent features for each base learner, leveraging a combined heuristic-metaheuristic approach that integrates the heuristic knowledge of Pearson correlation coefficient with the metaheuristic-driven grey wolf optimizer. The second objective is to aggregate the decision outcomes of the various base learners through ensemble learning, aimed at constructing a robust prediction model. The performance of the EHMFFL algorithm is rigorously assessed using the Cleveland and Statlog datasets yielding remarkable results with an accuracy of 91.8% and 88.9%, respectively, surpassing state-of-the-art machine learning, ensemble learning, and feature selection techniques in heart disease diagnosis. These findings underscore the potential of the EHMFFL algorithm in enhancing diagnostic accuracy for heart disease and providing valuable support to clinicians in making more informed decisions regarding patient care.

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FPGA Design and Hardware Implementation of Heart Disease Diagnosis System Based on NVG-RAM Classifier

Cited by 18 publications

References 6 publications

Heart Disease Prediction Using Glowworm Swarm Optimization and Support Vector Machine Classifier

Heart Disease Prediction Using Glowworm Swarm Optimization and Support Vector Machine Classifier

EMG Signals Classification of Wide Range Motion Signals for Prosthetic Hand Control

EHMFFL: Ensemble Heuristic-Metaheuristic Feature Fusion Learning Algorithm for Heart Disease Diagnosis

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