Detection of heart disease employing Recurrent CONVoluted neural networks (Rec-CONVnet) for effectual classification process in smart medical application

Mishra, Jyoti; Tiwari, Mahendra; Singh, Sanjay T.; Goswami, Sanjay Kumar

doi:10.1109/icrtcst54752.2022.9782009

Cited by 3 publications

(3 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Random Forest (RF) model attained an accuracy of 86% using 608 features and achieved 85% accuracy with 30 features. Mishra et al [ 144 ] introduced an innovative application within the medical Internet of Things (IoMT) domain. They utilized a Recurrent convolutional neural network (Rec-CONVnet) to accurately estimate the risk of heart disease.…”

Section: Reported Workmentioning

confidence: 99%

A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis

Xu,

Li,

Zhu

et al. 2024

Bioengineering

View full text Add to dashboard Cite

Disease diagnosis represents a critical and arduous endeavor within the medical field. Artificial intelligence (AI) techniques, spanning from machine learning and deep learning to large model paradigms, stand poised to significantly augment physicians in rendering more evidence-based decisions, thus presenting a pioneering solution for clinical practice. Traditionally, the amalgamation of diverse medical data modalities (e.g., image, text, speech, genetic data, physiological signals) is imperative to facilitate a comprehensive disease analysis, a topic of burgeoning interest among both researchers and clinicians in recent times. Hence, there exists a pressing need to synthesize the latest strides in multi-modal data and AI technologies in the realm of medical diagnosis. In this paper, we narrow our focus to five specific disorders (Alzheimer’s disease, breast cancer, depression, heart disease, epilepsy), elucidating advanced endeavors in their diagnosis and treatment through the lens of artificial intelligence. Our survey not only delineates detailed diagnostic methodologies across varying modalities but also underscores commonly utilized public datasets, the intricacies of feature engineering, prevalent classification models, and envisaged challenges for future endeavors. In essence, our research endeavors to contribute to the advancement of diagnostic methodologies, furnishing invaluable insights for clinical decision making.

show abstract

Section: Reported Workmentioning

confidence: 99%

A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis

Xu,

Li,

Zhu

et al. 2024

Bioengineering

View full text Add to dashboard Cite

show abstract

“…Jyoti Mishra et al [31] identified key features for predicting heart disease using ML techniques. For the discovery of heart illnesses, a medical internet of things architecture based on recurrent neural networks (Rec-CONVnet) has been proposed.…”

Section: Related Workmentioning

confidence: 99%

Untitled

2023

Inf. Sci. Lett.

View full text Add to dashboard Cite

Based on the data of the world health organization (WHO), diagnosing heart disease is a great task, as heart disease (HD) is the most prevalent disease worldwide. We suggested a method based on heart sounds to deal with this difficult issue because the heart sound (HS) is an essential component for detecting heart conditions. A feature extraction technique and a classifier are used in the suggested strategy. We use the GoogLeNet convolutional neural network (CNN) architecture with some modifications to separate the most crucial attributes of HS, and the heart condition is classified as diseased or not diseased based on these attributes. The model is trained using the AdaBelief optimizer to tune the parameters of our modified GoogLeNet architecture. The model was trained and validated utilising various datasets from PhysioNet 2016. Additional training samples were provided by integrating the PASCAL dataset with the PhysioNet 2016 dataset. Additionally, the variety of samples from various sources enabled our system to learn about sounds from everyday life more accurately. Our results indicated that using a modified GoogLeNet architecture with the AdaBelief optimizer, the trained model obtained test accuracy of 100% and 99.9% on unseen HS recordings from PhysioNet and the merged datasets, respectively. By comparing our proposed model with the highest-scoring methods listed on the official PhysioNet website in these datasets, the results show significantly improved.

show abstract

“…Therefore, an IoMT architecture utilizing Recurrent CONVoluted neural networks (Rec-CONVnet) is proposed for the diagnosis of heart disease in order to increase prediction precision. Gradient-based learning is a key component of Rec-CONVnet's learning strategy, although it can easily get caught in local minima [20]. To categorize cardiac illness, the fuzzy proportional integral and derivative (Fuzzy PID) controller was utilized in the study together with a support vector machine.…”

Section: Related Workmentioning

confidence: 99%

Modified Self-Adaptive Bayesian Algorithm for Smart Heart Disease Prediction in IoT System

et al. 2022

View full text Add to dashboard Cite

Heart disease (HD) has surpassed all other causes of death in recent years. Estimating one’s risk of developing heart disease is difficult, since it takes both specialized knowledge and practical experience. The collection of sensor information for the diagnosis and prognosis of cardiac disease is a recent application of Internet of Things (IoT) technology in healthcare organizations. Despite the efforts of many scientists, the diagnostic results for HD remain unreliable. To solve this problem, we offer an IoT platform that uses a Modified Self-Adaptive Bayesian algorithm (MSABA) to provide more precise assessments of HD. When the patient wears the smartwatch and pulse sensor device, it records vital signs, including electrocardiogram (ECG) and blood pressure, and sends the data to a computer. The MSABA is used to determine whether the sensor data that has been obtained is normal or abnormal. To retrieve the features, the kernel discriminant analysis (KDA) is used. By contrasting the suggested MSABA with existing models, we can summarize the system’s efficacy. Findings like accuracy, precision, recall, and F1 measures show that the suggested MSABA-based prediction system outperforms competing approaches. The suggested method demonstrates that the MSABA achieves the highest rate of accuracy compared to the existing classifiers for the largest possible amount of data.

show abstract

Detection of heart disease employing Recurrent CONVoluted neural networks (Rec-CONVnet) for effectual classification process in smart medical application

Cited by 3 publications

References 15 publications

A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis

A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis

Untitled

Modified Self-Adaptive Bayesian Algorithm for Smart Heart Disease Prediction in IoT System

Contact Info

Product

Resources

About