An intelligent heart disease prediction system based on swarm-artificial neural network

Nandy, Sudarshan; Adhikari, Mainak; Balasubramanian, Venki; Menon, Varun G.; Li, Xingwang; Zakarya, Muhammad

doi:10.1007/s00521-021-06124-1

Cited by 73 publications

(42 citation statements)

References 25 publications

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“…The experimental results proved the robustness and effectiveness in the prediction of cardiac disorders. Nandy et al, (2021) (Nandy et al, 2021) provided a framework for heart disease prediction using the Swarm-ANN method. The proposed methodology outperformed existing works in terms of prediction accuracy, precision, and recall.…”

Section: Related Workmentioning

confidence: 99%

An optimal multi-disease prediction framework using hybrid machine learning techniques

Gupta¹,

Singh²

2022

KJS publishes peer-review articles in Mathematics, Computer Sci

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The prediction of lifestyle diseases is a vital domain in healthcare informatics research. This task is primarily achieved using the widely available machine learning algorithms. However, the highdimensionality of data amplifies the computation complexity and significantly reduces the models’ efficiency. Conspicuously, we presented a multi-disease prediction strategy for intelligent decision support using ensemble learning. The proposed work leverages genetic algorithm-based recursive feature elimination and AdaBoost to predict two prominent lifestyle diseases. Alongside the proposed approach, various benchmark machine learning techniques are also trained and validated using selected features under k-fold cross-validation. The results reveal the effectiveness of the proposed methodology in predicting multiple diseases in comparison to past works.

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Section: Related Workmentioning

confidence: 99%

An optimal multi-disease prediction framework using hybrid machine learning techniques

Gupta¹,

Singh²

2022

KJS publishes peer-review articles in Mathematics, Computer Sci

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“…In addition, neural networks are widely employed [ 13 , 16 ]. To predict cardiovascular heart disease, Nandy et al [ 14 ] employed a swarm-artificial neural network. The goal of the research was to increase accuracy.…”

Section: Related Studiesmentioning

confidence: 99%

Computer‐Aided Diagnostics of Heart Disease Risk Prediction Using Boosting Support Vector Machine

Owusu

Boakye-Sekyerehene

Appati

et al. 2021

Computational Intelligence and Neuroscience

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Heart diseases are a leading cause of death worldwide, and they have sparked a lot of interest in the scientific community. Because of the high number of impulsive deaths associated with it, early detection is critical. This study proposes a boosting Support Vector Machine (SVM) technique as the backbone of computer-aided diagnostic tools for more accurately forecasting heart disease risk levels. The datasets which contain 13 attributes such as gender, age, blood pressure, and chest pain are taken from the Cleveland clinic. In total, there were 303 records with 6 tuples having missing values. To clean the data, we deleted the 6 missing records through the listwise technique. The size of data, and the fact that it is a purely random subset, made this approach have no significant effect for the experiment because there were no biases. Salient features are selected using the boosting technique to speed up and improve accuracies. Using the train/test split approach, the data is then partitioned into training and testing. SVM is then used to train and test the data. The C parameter is set at 0.05 and the linear kernel function is used. Logistic regression, Nave Bayes, decision trees, Multilayer Perceptron, and random forest were used to compare the results. The proposed boosting SVM performed exceptionally well, making it a better tool than the existing techniques.

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“…The prevalent expansion of IoT and its application in medicinal research enhanced the efficiency of distant health monitoring systems [5]. IoT is regarded as a compendium of several physical materials to detect the physical events without any interruptions [6]. By utilizing IoT technology, Cardiovascular Disease (CVD) monitoring system can collect and transfer the physical variables of a patient to a distant healthcare facility center on real-time basis.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Enabled Intelligent Healthcare Management System in Smart Cities Environment

Mengash¹,

Alharbi²,

Alotaibi³

et al. 2023

Computers, Materials &Amp; Continua

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In recent times, cities are getting smart and can be managed effectively through diverse architectures and services. Smart cities have the ability to support smart medical systems that can infiltrate distinct events (i.e., smart hospitals, smart homes, and community health centres) and scenarios (e.g., rehabilitation, abnormal behavior monitoring, clinical decision-making, disease prevention and diagnosis postmarking surveillance and prescription recommendation). The integration of Artificial Intelligence (AI) with recent technologies, for instance medical screening gadgets, are significant enough to deliver maximum performance and improved management services to handle chronic diseases. With latest developments in digital data collection, AI techniques can be employed for clinical decision making process. On the other hand, Cardiovascular Disease (CVD) is one of the major illnesses that increase the mortality rate across the globe. Generally, wearables can be employed in healthcare systems that instigate the development of CVD detection and classification. With this motivation, the current study develops an Artificial Intelligence Enabled Decision Support System for CVD Disease Detection and Classification in e-healthcare environment, abbreviated as AIDSS-CDDC technique. The proposed AIDSS-CDDC model enables the Internet of Things (IoT) devices for healthcare data collection. Then, the collected data is saved in cloud server for examination. Followed by, training

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An intelligent heart disease prediction system based on swarm-artificial neural network

Cited by 73 publications

References 25 publications

An optimal multi-disease prediction framework using hybrid machine learning techniques

An optimal multi-disease prediction framework using hybrid machine learning techniques

Computer‐Aided Diagnostics of Heart Disease Risk Prediction Using Boosting Support Vector Machine

Deep Learning Enabled Intelligent Healthcare Management System in Smart Cities Environment

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