A Deep Learning Framework for Prediction of Cardiopulmonary Arrest

Sirisha Potluri; Bikash Chandra Sahoo; Sandeep Kumar Satapathy; Shruti Mishra; Janjhyam Venkata Naga Ramesh; Sachi Nandan Mohanty

doi:10.4108/eetpht.10.5420

Authors

Sirisha Potluri ICFAI Foundation for Higher Education
Bikash Chandra Sahoo Vellore Institute of Technology University
Sandeep Kumar Satapathy Yonsei University
Shruti Mishra Vellore Institute of Technology University
Janjhyam Venkata Naga Ramesh Koneru Lakshmaiah Education Foundation
Sachi Nandan Mohanty Vellore Institute of Technology University

DOI:

https://doi.org/10.4108/eetpht.10.5420

Keywords:

Heart Stroke, Adolescent, Neural Network, Predictive Models, Fibrinogen

Abstract

INTRODUCTION: The cardiopulmonary arrest is a major issue in any country. Gone are the days when it used to happen to those who are aged but now it is a major concern emerging among adolescents as well. According to the World Health Organization (WHO), cardiac arrest and stroke is still a major concern and remains a public health crisis. In past years India has witnessed many cases of heart related issues which used to occur predominantly among people having high cholesterol. But now the scenario has changed, and cases have been observed in people having normal cholesterol levels. There are several factors involved in heart stroke such as age, sex, blood pressure, etc. which are used by doctors to monitor and diagnose the same.

OBJECTIVES: This paper focuses on different predictive models and ways to improve the accuracy of prediction by analyzing datasets on how they affect the accuracy of certain algorithms.

METHODS: The factors contributing to heart issues can be used as a beacon to predict the stroke and help an individual to further consult a doctor beforehand. The idea is to target the datasets and the prediction algorithms of deep learning including advanced ones to improvise it and attain a better result.

RESULTS: This paper brings out the comparative analysis among neural network techniques like ANN, Transfer Learning, MAML and LRP in which ANN showed the best result by giving the highest accuracy of 94%.

CONCLUSION: Furthermore, it discusses a new attribute called “gamma prime fibrinogen” which could be used in the future to boost prediction performance.

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References

C Malode, K Bhargavi, B G Gunasheela, G kavana and R Sushmitha. Soft set and fuzzy rules enabled SVM approach for heart attack risk classification among adolescents. ICCUBEA, 2018.

F Bulut. Heart attack risk detection using Bagging Classifier. SIU. 2016. DOI: https://doi.org/10.1109/SIU.2016.7496164

D K Ravish, K J Shanthi, Nayana R Shenoy and S Nisargh. Heart function monitoring, prediction and prevention of heart attacks: using artificial neural networks. ICCCI. 2014. DOI: https://doi.org/10.1109/IC3I.2014.7019580

D Krithika and K Rohini. Ensemble Based Prediction of Cardiovascular Disease Using Bigdata analytics. IEEE. 2021. pp.42-46. DOI: https://doi.org/10.1109/ICCS54944.2021.00017

T Rakshit and A Shreshtha. Comparative Analysis and Implementation of Heart Stroke Prediction using Various Machine Learning Techniques. IJETR. 2021. 10(6), pp. 6-9.

J Yu, S Park, S H Kwan, K Cho and H Lee. AI-based stroke disease prediction system using ECG and PPG bio-signals. IEEE. 2022. 10(3), pp.23-38. DOI: https://doi.org/10.1109/ACCESS.2022.3169284

D Sharathchandra and M Ram. ML-based interactive disease prediction model. IEEE. 2022. DOI: https://doi.org/10.1109/DELCON54057.2022.9752947

A. Gavhane, G. Kokkula, I. Pandya and K. Devadkar. Prediction of Heart Disease Using Machine Learning. Second International Conference on Electronics, Communication and Aerospace Technology (ICECA). 2018. DOI: https://doi.org/10.1109/ICECA.2018.8474922

S. K. Satapathy, S. Mishra, P.K. Mallick, G.S. Chae. ADASYN and ABC Optimized RBF Convergence Network for Classification of Electroencephalograph Signal. Personal and Ubiquitous Computing, Springer. 2022. DOI: https://doi.org/10.1007/s00779-021-01533-4

S Rajamhoana, C Devi, K Umamaheswari, R Kiruba, K Karunya and R Deepika. Analysis of Neural Networks Based Heart Disease Prediction System. 11th International Conference on Human System Interaction (ICHSI). 2018. pp. 18-25. DOI: https://doi.org/10.1109/HSI.2018.8431153

Paranthaman, Yaathash and Sanjairam. Cardiovascular Disease Prediction using Deep Learning, 6th International Conference on Trends in Electronics and Informatics (ICOEI), 2022.

A Kumar, K Rathor, S Vaddi, D Patel, P Vanjarapu and M Maddi. ECG Based Early Heart Attack Prediction Using Neural Networks. IEEE. 2022. DOI: https://doi.org/10.1109/ICESC54411.2022.9885448

J Digumarthi, V Gayathri and R Pitchai. Early Prediction of Cardiac Arrhythmia using Novel Bio-inspired Algorithms. 8th International Conference on Smart Structures and Systems (ICSSS). 2022. pp.1-4. DOI: https://doi.org/10.1109/ICSSS54381.2022.9782178

S. Mishra, S.K. Satapathy, S.N. Mohanty and C. R. Patnaik. A DM-ELM based classifier for EEG brain signal classification for epileptic seizure detection. Communicative and Integrative Biology. 2023. 16:1. DOI: https://doi.org/10.1080/19420889.2022.2153648

H Puri, J Chaudhary, K R Raghvendra, R Mantri and K Bingi. Prediction of stroke using SVM algorithm. 8th International conference on smart computing and communications (ICSCC). 2021. 5(9), pp.7-18.

M Salman, N Shila, K Hasan, P Ahmed, S Khushbu and S Noori. Data Mining Technique for Prediction System of Heart Disease Using Associative Classifications. 12th International Conference on Computing Communication and Networking Technologies (ICCNT). 2021. pp. 1-5. DOI: https://doi.org/10.1109/ICCCNT51525.2021.9579825

S. K. Satapathy, S. Saravanan, S. Mishra, S. N. Mohanty, A Comparative Analysis of Multidimensional COVID‐19 Poverty Determinants: An Observational Machine Learning Approach, New Generation Computing, Springer. 2023. DOI: https://doi.org/10.1007/s00354-023-00203-8

S Krishnan, P Magalingam and R Ibrahim. Hybrid deep learning model using recurrent neural network and gated recurrent unit for heart disease prediction. IJECE. 2021. DOI: https://doi.org/10.11591/ijece.v11i6.pp5467-5476

N. Panda, S. K. Satapathy, S. Mishra, P. K. Mallik. Empirical Study on Different Feature Selection and Classification Algorithms for Prediction of Hepatitis Disease. Technical Advancements of Machine Learning in Healthcare 936, 75. Springer. 2021. DOI: https://doi.org/10.1007/978-981-33-4698-7_4

H Tiwari. Early Prediction of heart disease using deep learning approach. CIIRC. 2022. DOI: https://doi.org/10.1016/B978-0-12-824145-5.00014-9

S Priscila and M Hemalatha. Improving the Performance of Entropy Ensembles of Neural Networks on Classification of Heart Disease Prediction. IJPAM. 2017. 117(7), pp.14-19.

D Swain, S Pani and D Swain. An efficient system for the prediction of coronary artery disease using a dense neural network with hyperparameter tuning. IJITEE. 2019. 8(6), pp. 54-67.

Y Xie, H Yang, X Yuan and Q He. Stroke prediction from electrocardiograms by deep neural network. Multimed Tools Application (MTA), 2021. DOI: https://doi.org/10.1007/s11042-020-10043-z

M Chae, S Han, H Gil, N Cho and H Lee. Prediction of In-Hospital Cardiac Arrest Using Shallow and Deep Learning. 2021. 11(7), pp. 25-35. DOI: https://doi.org/10.3390/diagnostics11071255