Smart Health Monitoring for Predicting Heart Disease using IoT-Fog-Cloud Computing Model
Received: 27 December 2024 | Revised: 2 February 2025 | Accepted: 5 February 2025 | Online: 29 March 2025
Corresponding author: Hafsat Jalo Suleiman
Abstract
Cloud computing enables access to various resources online, supporting services across numerous sectors. However, meeting real-time demands in IoT-based computing is challenging due to high latency issues. This is particularly problematic for low-latency applications, such as health monitoring and traffic surveillance, which require fast processing of large datasets. Performance drop occurs when data moves between central databases and cloud data centers. Edge and fog computing have emerged as new solutions to address this. These models place computing resources closer to users, significantly reducing latency and energy consumption while improving data processing efficiency. This paper presents a prediction system utilizing a fog-cloud framework, combining machine learning and deep learning with wearable IoT devices for real-time cardiovascular disease prediction. The system is trained using cardiovascular data from Gombe State, Nigeria, and evaluated based on energy consumption, precision, accuracy, recall, F1 score, and AUC. The proposed Optimized Naïve Bayes Random Forest (ONBRF) model offers a reliable and energy efficient approach to predicting heart disease.
Keywords:
IoT, heart disease, fog computing, cloud computing, accuracy, energy consumptionDownloads
References
E. Moghadas, J. Rezazadeh, and R. Farahbakhsh, "An IoT patient monitoring based on fog computing and data mining: Cardiac arrhythmia usecase," Internet of Things, vol. 11, Sep. 2020, Art. no. 100251.
A. Rejeb, K. Rejeb, S. Simske, H. Treiblmaier, and S. Zailani, "The big picture on the internet of things and the smart city: a review of what we know and what we need to know," Internet of Things, vol. 19, Aug. 2022, Art. no. 100565.
N. Y. Philip, J. J. P. C. Rodrigues, H. Wang, S. J. Fong, and J. Chen, "Internet of Things for In-Home Health Monitoring Systems: Current Advances, Challenges and Future Directions," IEEE Journal on Selected Areas in Communications, vol. 39, no. 2, pp. 300–310, Oct. 2021.
Md. Asif-Ur-Rahman et al., "Toward a Heterogeneous Mist, Fog, and Cloud-Based Framework for the Internet of Healthcare Things," IEEE Internet of Things Journal, vol. 6, no. 3, pp. 4049–4062, Jun. 2019.
K. S. Awaisi, S. Hussain, M. Ahmed, A. A. Khan, and G. Ahmed, "Leveraging IoT and Fog Computing in Healthcare Systems," IEEE Internet of Things Magazine, vol. 3, no. 2, pp. 52–56, Jun. 2020.
S. K. Sood and I. Mahajan, "IoT-Fog-Based Healthcare Framework to Identify and Control Hypertension Attack," IEEE Internet of Things Journal, vol. 6, no. 2, pp. 1920–1927, Apr. 2019.
S. Rastegar, H. Gholam Hosseini, and A. Lowe, "Hybrid CNN-SVR Blood Pressure Estimation Model Using ECG and PPG Signals," Sensors, vol. 23, no. 3, Jan. 2023, Art. no. 1259.
S. Tuli et al., "HealthFog: An ensemble deep learning based Smart Healthcare System for Automatic Diagnosis of Heart Diseases in integrated IoT and fog computing environments," Future Generation Computer Systems, vol. 104, pp. 187–200, Mar. 2020.
H. F. Atlam, R. J. Walters, and G. B. Wills, "Fog Computing and the Internet of Things: A Review," Big Data and Cognitive Computing, vol. 2, no. 2, Jun. 2018, Art. no. 10.
A. A. Mutlag, M. K. Abd Ghani, N. Arunkumar, M. A. Mohammed, and O. Mohd, "Enabling technologies for fog computing in healthcare IoT systems," Future Generation Computer Systems, vol. 90, pp. 62–78, Jan. 2019.
A. Jain, M. Ahirwar, and R. Pandey, "A Review on Intutive Prediction of Heart Disease Using Data Mining Techniques," International Journal of Computer Sciences and Engineering, 2019.
N. Absar et al., "The Efficacy of Machine-Learning-Supported Smart System for Heart Disease Prediction," Healthcare, vol. 10, no. 6, Jun. 2022, Art. no. 1137.
S. Guruprasad, V. L. Mathias, and W. Dcunha, "Heart Disease Prediction Using Machine Learning Techniques," in 2021 5th International Conference on Electrical, Electronics, Communication, Computer Technologies and Optimization Techniques (ICEECCOT), Mysuru, India, Dec. 2021, pp. 762–766.
G. Choudhary and S. Narayan Singh, "Prediction of Heart Disease using Machine Learning Algorithms," in 2020 International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE), Bengaluru, India, Oct. 2020, pp. 197–202.
S. Kumari, M. Bhatia, and G. Stea, "Fog-Computing Based Healthcare Framework for Predicting Encephalitis Outbreak," Big Data Research, vol. 29, Aug. 2022, Art. no. 100330.
A. Pati, M. Parhi, M. Alnabhan, B. K. Pattanayak, A. K. Habboush, and M. K. Al Nawayseh, "An IoT-Fog-Cloud Integrated Framework for Real-Time Remote Cardiovascular Disease Diagnosis," Informatics, vol. 10, no. 1, Mar. 2023, Art. no. 21.
O. R. Olaniran and M. A. A. Abdullah, "Bayesian weighted random forest for classification of high-dimensional genomics data," Kuwait Journal of Science, vol. 50, no. 4, pp. 477–484, Oct. 2023.
O. R. Olaniran and A. R. R. Alzahrani, "On the Oracle Properties of Bayesian Random Forest for Sparse High-Dimensional Gaussian Regression," Mathematics, vol. 11, no. 24, Jan. 2023, Art. no. 4957.
A. Arjmand and N. Giannakeas, "Fat Quantitation in Liver Biopsies Using a Pretrained Classification Based System," Engineering, Technology & Applied Science Research, vol. 8, no. 6, pp. 3550–3555, Dec. 2018.
A. Satty, M. M. Y. Salih, A. A. Hassaballa, E. A. E. Gumma, A. Abdallah, and G. S. M. Khamis, "Comparative Analysis of Machine Learning Algorithms for Investigating Myocardial Infarction Complications," Engineering, Technology & Applied Science Research, vol. 14, no. 1, pp. 12775–12779, Feb. 2024.
S. M. Alanazi and G. S. M. Khamis, "Optimizing Machine Learning Classifiers for Enhanced Cardiovascular Disease Prediction," Engineering, Technology & Applied Science Research, vol. 14, no. 1, pp. 12911–12917, Feb. 2024.
"Optimized NaiveBayes Random Forest Heart Disease Prediction." https://rid4stat.shinyapps.io/FOGCHD.
A. Janosi, W. Steinbrunn, M. Pfisterer, and R. Detrano, "Heart Disease." UCI Machine Learning Repository, 1989.
S. A. A. Shah, A. H. Saleh, M. Ebrahimian, and R. Kashef, "Early Detection of Heart Disease Using Advances of Machine Learning for Large-Scale Patient Datasets," in 2022 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), Halifax, Canada, Sep. 2022, pp. 274–280.
N. Chandrasekhar and S. Peddakrishna, "Enhancing Heart Disease Prediction Accuracy through Machine Learning Techniques and Optimization," Processes, vol. 11, no. 4, Apr. 2023, Art. no. 1210.
A. K. Gárate-Escamila, A. Hajjam El Hassani, and E. Andrès, "Classification models for heart disease prediction using feature selection and PCA," Informatics in Medicine Unlocked, vol. 19, Jan. 2020, Art. no. 100330.
G. Rajkumar, T. Gayathri Devi, and A. Srinivasan, "Heart disease prediction using IoT based framework and improved deep learning approach: Medical application," Medical Engineering & Physics, vol. 111, Jan. 2023, Art. no. 103937.
O. Terrada, B. Cherradi, A. Raihani, and O. Bouattane, "Atherosclerosis disease prediction using Supervised Machine Learning Techniques," in 2020 1st International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET), Meknes, Morocco, Apr. 2020, pp. 1–5.
A. Tiwari, A. Chugh, and A. Sharma, "Ensemble framework for cardiovascular disease prediction," Computers in Biology and Medicine, vol. 146, Jul. 2022, Art. no. 105624.
G. N. Ahmad, H. Fatima, S. Ullah, A. Salah Saidi, and Imdadullah, "Efficient Medical Diagnosis of Human Heart Diseases Using Machine Learning Techniques With and Without GridSearchCV," IEEE Access, vol. 10, pp. 80151–80173, 2022.
M. Kavitha, G. Gnaneswar, R. Dinesh, Y. R. Sai, and R. S. Suraj, "Heart Disease Prediction using Hybrid machine Learning Model," in 2021 6th International Conference on Inventive Computation Technologies (ICICT), Coimbatore, India, Jan. 2021, pp. 1329–1333.
S. P. Patro, G. S. Nayak, and N. Padhy, "Heart disease prediction by using novel optimization algorithm: A supervised learning prospective," Informatics in Medicine Unlocked, vol. 26, Jan. 2021, Art. no. 100696.
F. Rustam, A. Ishaq, K. Munir, M. Almutairi, N. Aslam, and I. Ashraf, "Incorporating CNN Features for Optimizing Performance of Ensemble Classifier for Cardiovascular Disease Prediction," Diagnostics, vol. 12, no. 6, Jun. 2022, Art. no. 1474.
E. A. Ogundepo and W. B. Yahya, "Performance analysis of supervised classification models on heart disease prediction," Innovations in Systems and Software Engineering, vol. 19, no. 1, pp. 129–144, Mar. 2023.
A. K. Dubey, A. K. Sinhal, and R. Sharma, "An Improved Auto Categorical PSO with ML for Heart Disease Prediction," Engineering, Technology & Applied Science Research, vol. 12, no. 3, pp. 8567–8573, Jun. 2022.
R. Rajendran and A. Karthi, "Heart disease prediction using entropy based feature engineering and ensembling of machine learning classifiers," Expert Systems with Applications, vol. 207, Nov. 2022, Art. no. 117882.
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Copyright (c) 2025 Hafsat Jalo Suleiman, Isredza Rahmi A. Hamid, Oyebayo Ridwan Olaniran
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