Heart disease detection using hybrid of bacterial foraging and particle swarm optimization

Kora, Padmavathi; Abraham, Ajith; Meenakshi, K.

doi:10.1007/s12530-019-09312-6

Cited by 29 publications

(12 citation statements)

References 36 publications

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“…They focused their study on the subject of protein structure and function structure prediction. Healthcare experts developed their own method for multimodal issues by studying how plants grow [ 8 ]. They chose the issue of specific proteins structure prediction and termed it image processing.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] An Image Processing Approach for Detection of Prenatal Heart Disease

Selvan

Thangaraj

Isaac

et al. 2022

BioMed Research International

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Prenatal heart disease, generally known as cardiac problems (CHDs), is a group of ailments that damage the heartbeat and has recently now become top deaths worldwide. It connects a plethora of cardiovascular diseases risks to the urgent in need of accurate, trustworthy, and effective approaches for early recognition. Data preprocessing is a common method for evaluating big quantities of information in the medical business. To help clinicians forecast heart problems, investigators utilize a range of data mining algorithms to examine enormous volumes of intricate medical information. The system is predicated on classification models such as NB, KNN, DT, and RF algorithms, so it includes a variety of cardiac disease-related variables. It takes do with an entire dataset from the medical research database of patients with heart disease. The set has 300 instances and 75 attributes. Considering their relevance in establishing the usefulness of alternate approaches, only 15 of the 75 criteria are examined. The purpose of this research is to predict whether or not a person will develop cardiovascular disease. According to the statistics, naïve Bayes classifier has the highest overall accuracy.

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Section: Introductionmentioning

confidence: 99%

[Retracted] An Image Processing Approach for Detection of Prenatal Heart Disease

Selvan

Thangaraj

Isaac

et al. 2022

BioMed Research International

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“…Meanwhile, a risk level of heart ailment cannot be detected with this process. Kora et al 18 devised a bacterial foraging particle swarm optimization (BFPSO) approach for detecting heart disease. This method effectively optimized the ECG features, but this method required high training time.…”

Section: Motivationsmentioning

confidence: 99%

“…A different challenges facing by the heart ailment detection methods are described beneath: The hybrid BF‐PSO algorithm devised in Reference 18 provided fast and accurate results, but it failed to implement the system with very large databases as it enhances the detection potential of heart diseases. The statistical features of the introduced technique had a very effective role in attaining better outcomes along with mel frequency cepstral coefficients (MFCC) features. Moreover, this method had lesser computational complexity and was prepared to be executed on the embedded platforms, but a major constraint lies in gathering more data for each class 20 …”

Section: Motivationsmentioning

confidence: 99%

RideNN‐OptDRN: Heart disease detection using RideNN based feature fusion and optimized deep residual network

Merugula

Padmaja

Veerubommu³

2022

Concurrency and Computation

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Heart disease detection through early-stage syndrome remains as a main confront in present world situation. If it is not detected appropriate time, then this turns out to be the major cause of death. Several existing heart disease detection techniques are developed with lower detection performance and therefore it is very significant to introduce a novel heart disease detection model that poses the potential to detect heart disease from input data. A novel detection approach named, social water cycle algorithm-based deep residual network (SWCA-based DRN) is proposed for classification of heart disease. The developed SWCA algorithm is a newly designed by the hybridization of social optimization algorithm and water cycle algorithm. Here, an input data is initially preprocessed and the feature fusion procedure is carried out RV coefficient enabled rider optimization algorithm-based neural network. With the fused feature result, heart disease classification is performed utilizing a DRN classifier where training procedure of DRN is done by proposed optimization algorithm, named SWCA. Furthermore, developed SWCA-enabled DRN technique outperformed different other present heart disease detection approaches and attained superior performance concerning the performance measures, like testing accuracy, sensitivity, and specificity with highest values of 0.941, 0.954, and 0.925. K E Y W O R D S deep residual network, heart disease, rider optimization algorithm, social optimization algorithm, water cycle algorithm INTRODUCTIONA heart is a fundamental organ which expels blood, with life-giving oxygen and supplements to various body tissues. If pumping action of heart becomes ineffective, essential organs such as kidneys as well as brain suffering, and if the heart functioning ceases completely, demise happens in a minute. Heart ailment has been regarded as one of fatal ailments across the world. Life is entirely reliant itself on an effective functioning of heart.The various indications of heart disease encompass weakness of the physical body, fatigue, swollen feet, and shortness of breath. 1,2 In addition, one of the most human life-threatening diseases around a world is heart ailments, due to variations in lifestyle and lack of physical exercise, and obesity is more general among human lives. According to World Health Organization (WHO) data, human deaths are almost 31% of overall and out of which 85% is due to stroke and heart failure. About 17 million people are losing their life due to heart disease every year. 3 Moreover, heart disease is caused based on the threat factors in lifestyle behavior, like sex, age, cholesterol, family history, obesity or inhaling cigarette smoke, improper diet, blood sugar levels, alcohol, high blood pressure, eating foods that are high in fat, and body weight. Certain threat factors are submissive. Furthermore, heart ailment can be partitioned in seven different kinds, namely, arrhythmia, cardiomyopathy, angina pectoris, coronary heart infection, congenital

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“…In [36], the discrete wavelet transform (DWT) performance and SVM coronary heart diseases, decision tree (DT), K-nearest neighbor, and neural network probability classifiers were compared to identify normal and nonlinear techniques. One of the most useful nature-inspired optimization algorithms referred to as bacterial-foragingoptimization (BFO) was developed by Kora et al [37]. The BFO modifications with SVM on the MIT-BIH dataset produced precision levels of 98.9% and 99.3%.…”

Section: Related Workmentioning

confidence: 99%

“…A fully automated method for the identification of arrhythmias from signals obtained by an ECG system can be divided into four steps: (1) ECG signal preprocessing, (2) heartbeat segmentation, (3) feature extraction, and ( 4) classification [37]. An action is taken in each of the four steps, and the final goal is to discriminate/identify the type of heartbeat.…”

Section: Preliminaries and Datasetsmentioning

confidence: 99%

A Hybrid Heartbeats Classification Approach Based on Marine Predators Algorithm and Convolution Neural Networks

Houssein

AbdElminaam

Ibrahim³

et al. 2021

IEEE Access

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The electrocardiogram (ECG) is a non-invasive tool used to diagnose various heart conditions. Arrhythmia is one of the primary causes of cardiac arrest. Early ECG beat classification plays a significant role in diagnosing life-threatening cardiac arrhythmias. However, the ECG signal is very small, the anti-interference potential is low, and the noise is easily influenced. Thus, clinicians face challenges in diagnosing arrhythmias. Thus, a method to automatically identify and distinguish arrhythmias from the ECG signal is invaluable. In this paper, a hybrid approach based on marine predators algorithm (MPA) and convolutional neural network (CNN) called MPA-CNN is proposed to classify the non-ectopic, ventricular ectopic, supraventricular ectopic, and fusion ECG types of arrhythmia. The proposed approach is a combination of heavy feature extraction and classification techniques; hence, outperforms other existing classification approaches. Optimal characteristics were derived directly from the raw signal to decrease the time required for and complexity of the computation. Precision levels of 99.31%, 99.76%, and 99.47% were achieved by the proposed approach on the MIT-BIH,EDB, and INCART databases, respectively.

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Heart disease detection using hybrid of bacterial foraging and particle swarm optimization

Cited by 29 publications

References 36 publications

[Retracted] An Image Processing Approach for Detection of Prenatal Heart Disease

[Retracted] An Image Processing Approach for Detection of Prenatal Heart Disease

RideNN‐OptDRN: Heart disease detection using RideNN based feature fusion and optimized deep residual network

A Hybrid Heartbeats Classification Approach Based on Marine Predators Algorithm and Convolution Neural Networks

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