Abstract
Deep learning (DL) has various applications in different fields such as smart agriculture, smart cities, intelligent transportation system, industries, and smart healthcare systems, etc., and it has broken through the current technical bottleneck in different fields. With applications in diverse areas, medical field is one of the most important application of DL in which has been applied extensively. Heart disease is one of the most hazardous lethal diseases, and it must be recognized as early as possible in order to limit the damage caused by this disease. Based on Long short-term memory (LSTM) network and DL technology, a medical diagnosis model is established to carry out heart disease diagnosis. One of the traditional diagnosis methods of heart disease is to obtain the patient's heart structure image by echocardiography. The doctor then gets the information from the image for diagnosis purposes. The main limitation associated with this approach is that it is not time-effective. This study uses LSTM and DL technology to learn data features to diagnose heart disease. The integration of LSTM and DL technology is used to establish a diagnosis model that uses a decision support system for heart disease and to improve the accuracy of disease diagnosis. Further, another goal of this study is to promote the development of DL technology in disease diagnosis frameworks. The results obtained from the experiments revealed that the proposed approach is practical and better than the other approaches for the efficient identification of cardiac disease.
Similar content being viewed by others
Data Avaliability
The data used to support the finding are cited within the article.
References
Allen LA et al (2014) Decision making in advanced heart failure: a scientific statement from the American Heart Association. Circulation 125:1928–1952
Altaheri H, Muhammad G, Alsulaiman M, Amin SU, Altuwaijri GA, Abdul W, Bencherif MA, Faisal M (2021) Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: a review. Neural Comput Appl. https://doi.org/10.1007/s00521-021-06352-5
Arabasadi Z, Alizadehsani R, Roshanzamir M, Moosaei H, Yarifard AA (2017) Computer aided decision making for heart disease detection using hybrid neural network-genetic algorithm. Comput Methods Programs Biomed 141:19–26
Bei F (2019) Classification of arrhythmia based on wavelet transform and neural network. University of Electronic Science and technology
Chen C, Yuxiong S, Jincheng L et al (2021) Prediction of length of stay after total knee arthroplasty based on machine learning algorithm. Chin Tissue Eng Res 25(27):4300–4306
Da J, Yan J, Deng S, Wang Z (2020) A study on the prediction model of repeated hospitalization based on deep learning: a case study of heart disease. Data Anal Knowl Discov 47(11):67–77
Das R, Turkoglu I, Sengur A (2011) Efective diagnosis of heart disease through neural networks ensembles. Expert Syst Appl 36:7675–7680
Das R, Turkoglu I, Sengur A (2012) Efective diagnosis of heart disease through neural networks ensembles. Expert Syst Appl 36:7675–7680
Detrano R et al (2009) International application of a new probability algorithm for the diagnosis of coronary artery disease. Am J Cardiol 64:304–310
Duan L, Hongxin Z, Zhiqing L et al (2019) Arrhythmia recognition of ECG signal based on deep residual convolution neural network. J Biomed Eng 36(02):189–198
Feng S, Gong X, Cui Z, et al. (2019) Application of support vector machine and artificial neural network in risk prediction of late venous graft disease after coronary artery bypass grafting. China health statistics, 2019 (4).
Gudadhe M, Wankhade K, Dongre S (2010) In: 2010 International conference on computer and communication technology (ICCCT), pp. 741-745 (IEEE, New York)
Heidenreich PA, Trogdon JG, Khavjou OA et al (2011) Forecasting the future of cardiovascular disease in the United States: a policy statement from the American Heart Association. Circulation 123(8):933–944
Jian W, Xiaoqian Li (2019a) A new method of heart disease prediction based on feature combination and convolution neural network. J Nat Sci Heilongjiang Univ 36(01):119–124
Jian W, Xiaoqian Li (2019b) A novel method of prediction for heart disease based on revolution neural networks. J Nat Sci Heilongjiang Univ 036(001):115–120
Kahramanli H, Allahverdi N (2013) Design of a hybrid system for the diabetes and heart diseases. Expert Syst Appl 35:82–89
Khan MI, Jan MA, Muhammad Y, Do DT, Ur Rehman A, Mavromoustakis CX, Pallis E (2021) Tracking vital signs of a patient using channel state information and machine learning for a smart healthcare system. Neural Comput Appl. https://doi.org/10.1007/s00521-020-05631-x
Li X, Zhengpeng Z, Jiahua P et al (2019) Recognition of congenital heart disease heart sound signal based on wavelet cepstrum coefficient and probabilistic neural network. Biomedical 009(001):10–16
Li X (2019a). Recognition of congenital heart disease based on wavelet cepstrum coefficient and probabilistic neural network. Yunnan University
Li X (2019b) Research on heart disease prediction method based on convolution neural network. Northeast Forestry University
Liu C, Huang J, Dan H et al (2019) Design and implementation of elderly health monitoring software based on chronic disease prediction. Inf Commun 000(002):93–96
Ma G (2019) Research on ECG automatic recognition algorithm based on multimodal neural network. Beijing University of Posts and telecommunications
Mi B, Hong W, Song J, Wu S, Meng H (2019) Myocardial ischemia pre-diagnosis method based on infrared thermal imaging and BP neural network. Laser Optoelectron Progr 56(1):011101
Muhammad Y, Alshehri MD, Alenazy WM, Vinh Hoang T, Alturki R (2021) Identification of pneumonia disease applying an intelligent computational framework based on deep learning and machine learning techniques. Mob Info Syst. https://doi.org/10.1155/2021/9989237
Nandy S, Adhikari M, Balasubramanian V, Menon VG, Li X, Zakarya M (2021) An intelligent heart disease prediction system based on swarm-artificial neural network. Neural Comput Appl. https://doi.org/10.1007/s00521-021-06124-1
Olaniyi EO, Oyedotun OK, Adnan K (2015) Heart diseases diagnosis using neural networks arbitration. Int J Intel Syst Appl 7:72
Palaniappan S, Awang R (2012) In: 2012 IEEE/ACS international conference on computer systems and applications, pp. 108–115 (IEEE, New York)
Patil SB, Kumaraswamy Y (2009) Intelligent and effective heart attack prediction system using data mining and artificial neural network. Eur J Sci Res 31:642–656
Peng X, Yanping X, Ming L et al (2020) Detection of inferior myocardial infarction based on dense connected convolutional neural network. J Biomed Eng 1:142–149
Research on application of data mining algorithm in heart disease medical diagnosis system. Inner Mongolia University of Finance and economics, 2020.
Samuel OW, Asogbon GM, Sangaiah AK, Fang P, Li G (2017) An integrated decision support system based on ANN and Fuzzy_AHP for heart failure risk prediction. Expert Syst Appl 68:163–172
Samuel OW, Asogbon GM, Sangaiah AK, Fang P, Li G (2018) An integrated decision support system based on ANN and Fuzzy_AHP for heart failure risk prediction. Expert Syst Appl 68:163–172
Vanisree K, Singaraju J (2015) Decision support system for congenital heart disease diagnosis based on signs and symptoms using neural networks. Int J Comput Appl 19:6–12
Wen TC, Wang W, Zong R et al (2019) The application of convolutional neural network in the classification of heart sound signal of congenital heart disease. Comput Eng Appl 055(012):174–180
Wen X, Zeyang Y, Hailong Q et al (2020) application of artificial intelligence in congenital cardiology. Chin J Thorac Cardiovasc Surg 27(03):113–123
Wu X, Liu C, Wang L, Bilal M (2021) Internet of things-enabled real-time health monitoring system using deep learning. Neural Comput Appl. https://doi.org/10.1007/s00521-021-06440-6
Wu FA, Wang SC, Cai QW (2020) Automatic three vessel segmentation method for fetal echocardiography based on convolution neural network, cn111354005a
Yang H, Garibaldi JM (2015) A hybrid model for automatic identification of risk factors for heart disease. J Biomed Inform 58:S171–S182
Yang S, Binbin G, Chen S (2021) Arrhythmia detection based on wavelet decomposition and 1D googlenet. Acta Electronica Sinica 2021:1–10
Yao X, Guo Y, Xie W et al (2020) application of deep learning in auxiliary detection of congenital ventricular septal defect and atrial septal defect in children. Chin J Med Comput Imag 26(4):384–389
Zhu X, Zhu Y, Li L, Pan S, Tariq MU, Jan MA (2021) IoHT-enabled gliomas disease management using fog Computing computing for sustainable societies. Sustain Cities Soc 74:1032
Funding
The paper did not receive any financial support.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declared that they have no conflicts of interest in this work.
Ethical approval
This paper does not deal with any ethical problems.
Informed consent
We declare that all authors have informed Consent.
Additional information
Communicated by Tiancheng Yang.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Wang, J., Li, J., Wang, L. et al. Heart disease diagnosis using deep learning and cardiac color doppler ultrasound. Soft Comput 26, 10633–10642 (2022). https://doi.org/10.1007/s00500-022-06780-y
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-022-06780-y