Effective Classification of Heart Disease Using Convolutional Neural Networks

被引:2
作者
Lenin, ST. [1 ]
Venkatasalam, K. [2 ]
机构
[1] Mahendra Engn Coll, Dept Informat Technol, Namakkal, Tamil Nadu, India
[2] Mahendra Engn Coll, Dept Comp Sci & Engn, Namakkal, Tamil Nadu, India
关键词
Heart disease classification; Electrocardiogram signals; Sand cat swarm optimization; Generative adversarial network; Restricted Boltzmann machine; Principle component analysis; Classification; SYSTEM; MODEL;
D O I
10.1007/s00034-024-02851-7
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Accurate classification of heart disease is crucial for precise diagnosis, requiring the disease condition to be predicted with high accuracy. Deep learning-based diagnostic algorithms are developed to learn from and predict disease status using Electrocardiogram signals derived from extensive pathology data. This method can potentially save more lives. Utilizing a large number of Electronic Health Records, this research aims to explore the benefits of deep learning methods and Sand Cat Swarm Optimization (SCSO) for feature selection in predicting patient disease. A dataset containing pathogenic information for patients with various diseases is pre-processed, and the SCSO approach is used to choose the most important features for determining disease severity. A Convolutional Neural Network is used for classification in conjunction with Principal Component Analysis, a Restricted Boltzmann Machine, and deep convolutional Generative Adversarial Network models. These models are developed using the selected features for learning intricate correlations between the input features and the disease state. The effectiveness of methods is assessed with the help of parameters such like accuracy, precision, recall, and F1-score. The proposed model also offers additional benefits for heart disease prognosis, improving accuracy and reliability of disease prediction.
引用
收藏
页码:911 / 935
页数:25
相关论文
共 50 条
[41]   Generalization of Convolutional Neural Networks for ECG Classification Using Generative Adversarial Networks [J].
Shaker, Abdelrahman M. ;
Tantawi, Manal ;
Shedeed, Howida A. ;
Tolba, Mohamed F. .
IEEE ACCESS, 2020, 8 :35592-35605
[42]   Skin melanoma classification using ROI and data augmentation with deep convolutional neural networks [J].
Hosny, Khalid M. ;
Kassem, Mohamed A. ;
Foaud, Mohamed M. .
MULTIMEDIA TOOLS AND APPLICATIONS, 2020, 79 (33-34) :24029-24055
[43]   An automatic approach for heart failure typing based on heart sounds and convolutional recurrent neural networks [J].
Wang, Hui ;
Guo, Xingming ;
Zheng, Yineng ;
Yang, Yang .
PHYSICAL AND ENGINEERING SCIENCES IN MEDICINE, 2022, 45 (02) :475-485
[44]   Compression of Deep Convolutional Neural Networks Using Effective Channel Pruning [J].
Guo, Qingbei ;
Wu, Xiao-Jun ;
Zhao, Xiuyang .
IMAGE AND GRAPHICS, ICIG 2019, PT I, 2019, 11901 :760-772
[45]   Classification of Tumor Regions in Histopathological Images Using Convolutional Neural Networks [J].
Gunduz, Koray ;
Albayrak, Abdulkadir ;
Bilgin, Gokhan ;
Karsligil, M. Elif .
2018 26TH SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU), 2018,
[46]   Automated Classification of Body MRI Sequences Using Convolutional Neural Networks [J].
Kim, Boah ;
Mathai, Tejas Sudharshan ;
Helm, Kimberly ;
Mukherjee, Pritam ;
Liu, Jianfei ;
Summers, Ronald M. .
ACADEMIC RADIOLOGY, 2025, 32 (03) :1192-1203
[47]   Model-Aided Drone Classification Using Convolutional Neural Networks [J].
Karlsson, Alexander ;
Jansson, Magnus ;
Hamalainen, Mikael .
2022 IEEE RADAR CONFERENCE (RADARCONF'22), 2022,
[48]   Heartbeat Classification Using 1D Convolutional Neural Networks [J].
Shaker, Abdelrahman M. ;
Tantawi, Manal ;
Shedeed, Howida A. ;
Tolba, Mohamed F. .
PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON ADVANCED INTELLIGENT SYSTEMS AND INFORMATICS 2019, 2020, 1058 :502-511
[49]   Sparse Matrix Classification on Imbalanced Datasets Using Convolutional Neural Networks [J].
Pichel, Juan C. ;
Pateiro-Lopez, Beatriz .
IEEE ACCESS, 2019, 7 :82377-82389
[50]   Classification of polarimetric SAR images using compact convolutional neural networks [J].
Ahishali, Mete ;
Kiranyaz, Serkan ;
Ince, Turker ;
Gabbouj, Moncef .
GISCIENCE & REMOTE SENSING, 2021, 58 (01) :28-47