Arman Process Journal (APJ)
Quarterly Journal of Information and Communication Technology ​

Prediction of Cardiovascular Diseases Using Convolutional Neural Network Based on Internet of Things

Document Type : Original Research Article

Authors

Seyedeh Fatemeh Abdollahi 1
Seyed Ebrahim Dashti 2

1 Faculty of Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran

2 Faculty of Electrical and Computer Engineering, Jahrom Branch, Islamic Azad University

10.22034/apj.2025.725731
Abstract
One of the most important applications of the Internet of Things in the field of health is remote monitoring of patients. This technology allows doctors to check the health status of patients in real time, which is especially vital for people suffering from or prone to heart diseases. Prediction of cardiovascular diseases is known to be a complex challenge that faces low accuracy in existing models. In this research, a new recommender system for predicting cardiovascular diseases is proposed that uses a convolutional neural network to analyze physiological data of patients. Physiological data from patients are collected remotely through four biological sensors including ECG sensor, blood pressure sensor, heart rate sensor and blood sugar sensor. These data are then processed by an Arduino controller and the convolutional neural network model is used to predict cardiovascular disease. With outstanding capabilities in extracting local features and without the need for complex time sequence analysis, this model can effectively use fixed numerical data such as blood pressure, heart rate, and blood sugar to diagnose heart diseases. The experimental results showed that the convolutional neural network was able to effectively extract local and non-temporal features of the data and help the model achieve a prediction accuracy of 98.90%.

Highlights

[1] Shukla S, Neduncheliyan S. Predicting Cardiovascular Disease Through IoT and Deep Learning Methods. In2024 4th International Conference on Sustainable Expert Systems (ICSES) 2024 Oct 15 (pp. 250-255). IEEE.

[2] Thorpe KE. The future costs of obesity: National and state estimates of the impact of obesity on direct health care expenses. A collaborative report from United Health Foundation. 2009.

 

[3] Michie S, West R, Campbell R, Brown J, Gainforth H. ABC of behaviour change theories: an essential resource for researchers. Policy Makers and Practitioners. 2014;402.

 

[4] Linden G, Smith B, York J. Amazon. com recommendations: Item-to-item collaborative filtering. IEEE Internet computing. 2003 Feb 28;7(1):76-80.

 

[5] Konrad A, Bellotti V, Crenshaw N, Tucker S, Nelson L, Du H, Pirolli P, Whittaker S. Finding the adaptive sweet spot: Balancing compliance and achievement in automated stress reduction. InProceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems 2015 Apr 18 (pp. 3829-3838).

 

[6] Bharadhwaj H. Meta-learning for user cold-start recommendation. In2019 International Joint Conference on Neural Networks (IJCNN) 2019 Jul 14 (pp. 1-8). IEEE.

 

[7] Wei X, Rao C, Xiao X, Chen L, Goh M. Risk assessment of cardiovascular disease based on SOLSSA-CatBoost model. Expert systems with applications. 2023 Jun 1;219:119648.

 

[8] Paul B, Karn B. Heart disease prediction using scaled conjugate gradient backpropagation of artificial neural network. Soft Computing. 2023 May;27(10):6687-702.

[9] Malibari AA. An efficient IoT-Artificial intelligence-based disease prediction using lightweight CNN in healthcare system. Measurement: Sensors. 2023 Apr 1;26:100695.

[10] Shafiq M, Du C, Jamal N, Abro JH, Kamal T, Afsar S, Mia MS. Smart E‐Health System for Heart Disease Detection Using Artificial Intelligence and Internet of Things Integrated Next‐Generation Sensor Networks. Journal of Sensors. 2023;2023(1):6383099.

[11] Yaqoob MM, Nazir M, Khan MA, Qureshi S, Al-Rasheed A. Hybrid classifier-based federated learning in health service providers for cardiovascular disease prediction. Applied Sciences. 2023 Feb 1;13(3):1911.

[12] Safa M, Pandian A, Gururaj HL, Ravi V, Krichen M. Real time health care big data analytics model for improved QoS in cardiac disease prediction with IoT devices. Health and Technology. 2023 Jun;13(3):473-83.

 

[13] Ozcan M, Peker S. A classification and regression tree algorithm for heart disease modeling and prediction. Healthcare Analytics. 2023 Nov 1;3:100130.

 

[14] P. C. Bizimana, Z. Zhang, M. Asim, and A. A. A. El-Latif, “An effective machine learning-based model for an early heart disease prediction,” BioMed Res. Int., vol. 2023, pp. 1–11, Apr. 2023.

 

[15] Bhatt CM, Patel P, Ghetia T, Mazzeo PL. Effective heart disease prediction using machine learning techniques. Algorithms. 2023 Feb 6;16(2):88.

 

[16] Shukla PK, Stalin S, Joshi S, Shukla PK, Pareek PK. Optimization assisted bidirectional gated recurrent unit for healthcare monitoring system in big-data. Applied Soft Computing. 2023 May 1;138:110178.

 

[17] Lu H, Heyder M, Wenzel M, Albrecht NC, Langer D, Koelpin A. Accurate heart beat detection with Doppler radar using bidirectional GRU network. In2023 IEEE Radio and Wireless Symposium (RWS) 2023 Jan 22 (pp. 52-54). IEEE.

 

[18] Alkhawaldeh RS, Al-Ahmad B, Ksibi A, Ghatasheh N, Abu-Taieh EM, Aldehim G, Ayadi M, Alkhawaldeh SM. Convolution neural network bidirectional long short-term memory for heartbeat arrhythmia classification. International Journal of Computational Intelligence Systems. 2023 Dec 19;16(1):197.

 

[19] M. S. Islam et al., “HARDC : A novel ECG-based heartbeat classification method to detect arrhythmia using hierarchical attention based dual structured RNN with dilated CNN,” Neural Netw., vol. 162, pp. 271–287, May 2023.

 

[20] Zou Y, Yu X, Li S, Mou X, Du L, Chen X, Li Z, Wang P, Li X, Du M, Fang Z. A generalizable and robust deep learning method for atrial fibrillation detection from long-term electrocardiogram. Biomedical Signal Processing and Control. 2024 Apr 1;90:105797.

 

[21] Islam R, Abid MK, Aziz Y, Naeem A, Aslam N. Hybrid FNN-DNN approach for early detection of cardiac arrhythmia: A novel framework for enhanced diagnosis. VAWKUM Transactions on Computer Sciences. 2024 May 18;12(1):48-64.

 

[22] Nancy AA, Ravindran D, Vincent DR, Srinivasan K, Chang CY. Fog-based smart cardiovascular disease prediction system powered by modified gated recurrent unit. Diagnostics. 2023 Jun 15;13(12):2071.

 

[23] Talukdar J, Singh TP. Early prediction of cardiovascular disease using artificial neural network. Paladyn, Journal of Behavioral Robotics. 2023 Feb 17;14(1):20220107.

[24] Rajkumar G, Devi TG, Srinivasan A. Heart disease prediction using IoT based framework and improved deep learning approach: medical application. Medical engineering & physics. 2023 Jan 1;111:103937.

 

[25] Zafar S, Iftekhar N, Yadav A, Ahilan A, Kumar SN, Jeyam A. An IoT method for telemedicine: Lossless medical image compression using local adaptive blocks. IEEE Sensors Journal. 2022 Jun 24;22(15):15345-52.

 

[26] Dakshina DS, Della Reasa V, Bindhu A. Alzheimer disease detection via deep learning-based shuffle network. International Journal of Current Bio-Medical Engineering. 2023;1(1):9-15.

 

[27] Ahilan A, Rejula MA, Kumar SN, Kumar BM. Virtual reality sensor-based IoT embedded system for stress diagnosis. IEEE Sensors Journal. 2023 Sep 18;23(23):29425-33.

 

[28] Murugan TM, Jeyam A. IoT-enabled protein structure classification via CSA-PSO based CD4. 5 classifier. Int. J. Data Sci. Artif. Intell. 2023;1(2):41-51.

 

 

 

 

 

 

 

 

Keywords

Prediction Cardiovascular Disease
Convolutional Neural Network
Internet of Things
[1] Shukla S, Neduncheliyan S. Predicting Cardiovascular Disease Through IoT and Deep Learning Methods. In2024 4th International Conference on Sustainable Expert Systems (ICSES) 2024 Oct 15 (pp. 250-255). IEEE.
[2] Thorpe KE. The future costs of obesity: National and state estimates of the impact of obesity on direct health care expenses. A collaborative report from United Health Foundation. 2009.
 
[3] Michie S, West R, Campbell R, Brown J, Gainforth H. ABC of behaviour change theories: an essential resource for researchers. Policy Makers and Practitioners. 2014;402.
 
[4] Linden G, Smith B, York J. Amazon. com recommendations: Item-to-item collaborative filtering. IEEE Internet computing. 2003 Feb 28;7(1):76-80.
 
[5] Konrad A, Bellotti V, Crenshaw N, Tucker S, Nelson L, Du H, Pirolli P, Whittaker S. Finding the adaptive sweet spot: Balancing compliance and achievement in automated stress reduction. InProceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems 2015 Apr 18 (pp. 3829-3838).
 
[6] Bharadhwaj H. Meta-learning for user cold-start recommendation. In2019 International Joint Conference on Neural Networks (IJCNN) 2019 Jul 14 (pp. 1-8). IEEE.
 
[7] Wei X, Rao C, Xiao X, Chen L, Goh M. Risk assessment of cardiovascular disease based on SOLSSA-CatBoost model. Expert systems with applications. 2023 Jun 1;219:119648.
 
[8] Paul B, Karn B. Heart disease prediction using scaled conjugate gradient backpropagation of artificial neural network. Soft Computing. 2023 May;27(10):6687-702.
[9] Malibari AA. An efficient IoT-Artificial intelligence-based disease prediction using lightweight CNN in healthcare system. Measurement: Sensors. 2023 Apr 1;26:100695.
[10] Shafiq M, Du C, Jamal N, Abro JH, Kamal T, Afsar S, Mia MS. Smart E‐Health System for Heart Disease Detection Using Artificial Intelligence and Internet of Things Integrated Next‐Generation Sensor Networks. Journal of Sensors. 2023;2023(1):6383099.
[11] Yaqoob MM, Nazir M, Khan MA, Qureshi S, Al-Rasheed A. Hybrid classifier-based federated learning in health service providers for cardiovascular disease prediction. Applied Sciences. 2023 Feb 1;13(3):1911.
[12] Safa M, Pandian A, Gururaj HL, Ravi V, Krichen M. Real time health care big data analytics model for improved QoS in cardiac disease prediction with IoT devices. Health and Technology. 2023 Jun;13(3):473-83.
 
[13] Ozcan M, Peker S. A classification and regression tree algorithm for heart disease modeling and prediction. Healthcare Analytics. 2023 Nov 1;3:100130.
 
[14] P. C. Bizimana, Z. Zhang, M. Asim, and A. A. A. El-Latif, “An effective machine learning-based model for an early heart disease prediction,” BioMed Res. Int., vol. 2023, pp. 1–11, Apr. 2023.
 
[15] Bhatt CM, Patel P, Ghetia T, Mazzeo PL. Effective heart disease prediction using machine learning techniques. Algorithms. 2023 Feb 6;16(2):88.
 
[16] Shukla PK, Stalin S, Joshi S, Shukla PK, Pareek PK. Optimization assisted bidirectional gated recurrent unit for healthcare monitoring system in big-data. Applied Soft Computing. 2023 May 1;138:110178.
 
[17] Lu H, Heyder M, Wenzel M, Albrecht NC, Langer D, Koelpin A. Accurate heart beat detection with Doppler radar using bidirectional GRU network. In2023 IEEE Radio and Wireless Symposium (RWS) 2023 Jan 22 (pp. 52-54). IEEE.
 
[18] Alkhawaldeh RS, Al-Ahmad B, Ksibi A, Ghatasheh N, Abu-Taieh EM, Aldehim G, Ayadi M, Alkhawaldeh SM. Convolution neural network bidirectional long short-term memory for heartbeat arrhythmia classification. International Journal of Computational Intelligence Systems. 2023 Dec 19;16(1):197.
 
[19] M. S. Islam et al., “HARDC : A novel ECG-based heartbeat classification method to detect arrhythmia using hierarchical attention based dual structured RNN with dilated CNN,” Neural Netw., vol. 162, pp. 271–287, May 2023.
 
[20] Zou Y, Yu X, Li S, Mou X, Du L, Chen X, Li Z, Wang P, Li X, Du M, Fang Z. A generalizable and robust deep learning method for atrial fibrillation detection from long-term electrocardiogram. Biomedical Signal Processing and Control. 2024 Apr 1;90:105797.
 
[21] Islam R, Abid MK, Aziz Y, Naeem A, Aslam N. Hybrid FNN-DNN approach for early detection of cardiac arrhythmia: A novel framework for enhanced diagnosis. VAWKUM Transactions on Computer Sciences. 2024 May 18;12(1):48-64.
 
[22] Nancy AA, Ravindran D, Vincent DR, Srinivasan K, Chang CY. Fog-based smart cardiovascular disease prediction system powered by modified gated recurrent unit. Diagnostics. 2023 Jun 15;13(12):2071.
 
[23] Talukdar J, Singh TP. Early prediction of cardiovascular disease using artificial neural network. Paladyn, Journal of Behavioral Robotics. 2023 Feb 17;14(1):20220107.
[24] Rajkumar G, Devi TG, Srinivasan A. Heart disease prediction using IoT based framework and improved deep learning approach: medical application. Medical engineering & physics. 2023 Jan 1;111:103937.
 
[25] Zafar S, Iftekhar N, Yadav A, Ahilan A, Kumar SN, Jeyam A. An IoT method for telemedicine: Lossless medical image compression using local adaptive blocks. IEEE Sensors Journal. 2022 Jun 24;22(15):15345-52.
 
[26] Dakshina DS, Della Reasa V, Bindhu A. Alzheimer disease detection via deep learning-based shuffle network. International Journal of Current Bio-Medical Engineering. 2023;1(1):9-15.
 
[27] Ahilan A, Rejula MA, Kumar SN, Kumar BM. Virtual reality sensor-based IoT embedded system for stress diagnosis. IEEE Sensors Journal. 2023 Sep 18;23(23):29425-33.
 

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