Detection of Cardiac Abnormalities and Heart Disease Using Machine Learning Techniques
Keywords:
Detection System, Internet of Things, Vascular age of heart, Cardiac index, Monitoring System, Machine Learning, Deep learningAbstract
The prediction of heart disease is a very challenging task in medical science, and it is essential to predict accurately for deciding future treatment. Almost 30 million peoples have died due to heart failure and different heart diseases worldwide. Internet of Things (IoT) and machine learning are the techniques that help to understand the heart's current condition. Various researchers have developed a system for predicting heart disease using several methodologies, but still, it remains a challenge to predict the accurate state of heart disease. The cardiac index and vascular age of the heart are the two significant vitals that indicate the precise condition of the heart. In this paper, we proposed heart disease prediction using IoT and machine learning techniques. Initially, we collected data from numerous sensors such as sunroom BP for heart rate, max30100 for blood oxygen saturation, EEG for PT and QR intervals, etc. The hybrid feature extraction and selection techniques and numerous machine learning algorithms have been used for strong training model building. With extensive experimental analysis, few machine learning (ML) and deep learning techniques have been evaluated with the existing implementation. The Recurrent Neural Network (RNN) obtains better detection and classification accuracy than conventional machine learning (ML) techniques such as SVM (Support Vector Machine), Naive Bayes (NB), Random Forest (RF), etc.
Downloads
References
Humayun, A.I.; Ghaffarzadegan, S.; Ansari, M.I.; Feng, Z.; Hasan, T. Towards domain invariant heart sound abnormality detection using learnable filterbanks. IEEE J. Biomed. Health Inform. 2020, 24, 2189–2198.
Chowdhury, M.E.; Khandakar, A.; Alzoubi, K.; Mansoor, S.; Tahir, A.M.; Reaz, M.B.I.; Al-Emadi, N. Real-Time Smart-Digital Stethoscope System for Heart Diseases Monitoring. Sensors 2019, 19, 2781.
Tiwari, S.; Sharma, A.J.A.K.; Almustafa, K.M. Phonocardiogram Signal Based Multi-Class Cardiac Diagnostic Decision Support System. IEEE Access 2021, 9, 110710–110722.
Ukil, A.; Jara, A.J.; Marin, L. Data-Driven Automated Cardiac Health Management with Robust Edge Analytics and De-Risking. Sensors 2019, 19, 2733.
Maritsch, M.; Bérubé, C.; Kraus, M.; Lehmann, V.; Züger, T.; Feuerriegel, S.; Kowatsch, T.; Wortmann, F. Improving Heart Rate Variability Measurements from Consumer Smartwatches with Machine Learning. In Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers, London, UK, 9–13 September 2019; pp. 934–938.
Ren, Z.; Cummins, N.; Pandit, V.; Han, J.; Qian, K.; Schuller, B. Learning Image-based Representations for Heart Sound Classification. In Proceedings of the 2018 International Conference on Digital Health, Guilin, China, 30 November–1 December 2018; pp. 143–147
Sinharay, A.; Ghosh, D.; Deshpande, P.; Alam, S.; Banerjee, R.; Pal, A. Smartphone Based Digital Stethoscope for Connected Health—A direct Acoustic Coupling Technique. In Proceedings of the 2016 IEEE 1st International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE 2016), Crystal City, VA, USA, 19–22 October 2016; pp. 193–198
Gradl, S.; Wirth, M.; Zillig, T.; Eskofie, B.M. Visualization of Heart Activity in Virtual Reality: a Biofeedback Application using Wearable Sensors. In Proceedings of the 2018 IEEE 15th International Conference on Wearable and Implantable Body Sensor Networks (BSN 2018), Las Vegas, NV, USA, 4–7 March 2018; pp. 152–155
Doshi, M.; Fafadia, M.; Oza, S.; Deshmukh, A.; Pistolwala, S. Remote Diagnosis of Heart Disease Using Telemedicine. In Proceedings of the International Conference on Nascent Technologies in Engineering (ICNTE), Navi Mumbai, India, 4–5 January 2019; pp. 1–5.
Shuvo, S.B.; Ali, S.N.; Swapnil, S.I.; Al-Rakhami, M.S.; Gumaei, A. CardioXNet: A Novel Lightweight Deep Learning Framework for Cardiovascular Disease Classification Using Heart Sound Recordings. IEEE Access 2021, 9, 36955–36967.
Du, Z.; Yang, Y.; Zheng, J.; Li, Q.; Lin, D.; Lin, D.; Fan, J.; Cheng, W.; Chen, X.H.; Cai, Y. Accurate Prediction of Coronary Heart Disease for Patients With Hypertension From Electronic Health Records With Big Data and Machine-Learning Methods: Model Development and Performance Evaluation. JMIR Med. Inform. 2019, 8.
Amiri, A.M.; Abtahi, M.; Constant, N.; Mankodiya, K. Mobile Phonocardiogram Diagnosis in Newborns Using Support Vector Machine. Healthcare 2017, 5, 16.
Elgendi, M.; Fletcher, R.; Liang, Y.; Howard, N.; Lovell, N.H.; Abbott, D.; Lim, K.; Ward, R. The use of photoplethysmography for assessing hypertension. NPJ Digit. Med. 2019, 2, 60.
Liu, C.; Springer, D.; Li, Q.; Moody, B.; Juan, R.A.; Chorro, F.J.; Castells, F.; Roig, J.M.; Silva, I.; Johnson, A.E.W.; et al. An open access database for the evaluation of heart sound algorithms. Physiol. Meas. 2016, 37, 2181–2213.
Thiyagaraja, S.R.; Dantu, R.; Shrestha, P.L.; Chitnis, A.; Thompson, M.A.; Anumandla, P.T.; Sarma, T.; Dantu, S. A novel heart-mobile interface for detection and classification of heart sounds. Biomed. Signal Process. Control 2018, 45, 313–324.
Gómez-Quintana, S.; Schwarz, C.E.; Shelevytsky, I.; Shelevytska, V.; Semenova, O.; Factor, A.; Popovici, E.; Temko, A. A framework for ai-assisted detection of patent ductus arteriosus from neonatal phonocardiogram. Healthcare 2021, 9, 169.
Chorba, J.S.; Shapiro, A.M.; Le, L.; Maidens, J.; Prince, J.; Pham, S.; Kanzawa, M.M.; Barbosa, K.D.N.; Currie, C.; Brooks, C.; et al. Deep learning algorithm for automated cardiac murmur detection via a digital stethoscope platform. J. Am. Heart Assoc. 2021, 10, e019905.
Balakrishnand, D.; Rajkumart, T.D.; Dhanasekaran, S. An intelligent and secured heart rate monitoring system using IOT. Mater. Today Proc. 2020.
Kocyigit, Y.; Alkan, A.; Erol, H. Classification of EEG recordings by using fast independent component analysis and artificial neural network. J. Med Syst. 2008, 32, 17–20.
Mandic, D.P. A generalized normalized gradient descent algorithm. IEEE Signal Process. Lett. 2004, 11, 115–118.
Krizhevsky, A.; Sutskever, I.; Hinton, G. ImageNet Classification with Deep Convolutional Neural Networks. Neural Inf. Process. Syst. 2012, 25.
Saikat Bose, Tripti Arjariya, Anirban Goswami, Soumit Chowdhury Multi-Layer Digital Validation of Candidate Service Appointment with Digital Signature and Bio-Metric Authentication Approach International Journal of Computer Networks & Communications (IJCNC) Vol.14, No.5, September 2022 DOI: 10.5121/ijcnc.2022.14506
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.