CDPS-IoT: Cardiovascular Disease Prediction System Based on IoT using Machine Learning.

Jameel Ahamed; Abdul Manan Koli; Khaleel Ahmad; Mohd Alam Jama; Brij B. Gupta

doi:10.9781/ijimai.2021.09.002

Authors

Jameel Ahamed Maulana Azad National Urdu University
Abdul Manan Koli Maulana Azad National Urdu University
Khaleel Ahmad Maulana Azad National Urdu University
Mohd Alam Jama Maulana Azad National Urdu University
Brij B. Gupta National Institute of Technology Kurukshetra

DOI:

https://doi.org/10.9781/ijimai.2021.09.002

Keywords:

Cardiovascular Diseases, Cloud Computing, Internet of things, Machine Learning

Abstract

Internet of Things, Machine learning, and Cloud computing are the emerging domains of information communication and technology. These techniques can help to save the life of millions in the medical assisted environment and can be utilized in health-care system where health expertise is less available. Fast food consumption increased from the past few decades, which makes up cholesterol, diabetes, and many more problems that affect the heart and other organs of the body. Changing lifestyle is another parameter that results in health issues including cardio-vascular diseases. Affirming to the World Health Organization, the cardiovascular diseases, or heart diseases lead to more death than any other disease globally. The objective of this research is to analyze the available data pertaining to cardiovascular diseases for prediction of heart diseases at an earlier stage to prevent it from occurring. The dataset of heart disease patients was taken from Jammu and Kashmir, India and stored over the cloud. Stored data is then pre-processed and further analyzed using machine learning techniques for the prediction of heart diseases. The analysis of the dataset using numerous machines learning techniques like Random Forest, Decision Tree, Naive based, K-nearest neighbors, and Support Vector Machine revealed the performance metrics (F1 Score, Precision and Recall) for all the techniques which shows that Naive Bayes is better without parameter tuning while Random Forest algorithm proved as the best technique with hyperparameter tuning. In this paper, the proposed model is developed in such a systematic way that the clinical data can be obtained through the use of IoT with the help of available medical sensors to predict cardiovascular diseases on a real-time basis.

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References

V. Fuster, B. B. Kelly, eds., “Promoting cardiovascular health in the developing world: a critical challenge to achieve global health,” National Academies Press, 2010, pp 465.

P. Kaur, R. Kumar, M. Kumar, “A healthcare monitoring system using random forest and internet of things (IoT)”, 2019, pp. 19905-1916.

I. Cvitić, D. Peraković, M. Periša, M. Botica, “Novel approach for detection of IoT generated DDoS traffic,” Wireless Networks, 2019, pp. 1-14, doi: 10.1007/s11276-019-02043-1.

A. Tewari, B.B. Gupta, “Security, privacy and trust of different layers in Internet-of-Things (IoTs) framework,” Future generation computer systems”, 2002, pp 909-920.

I. Cvitic, D. Perakovic, M. Perisa,M. Botica, “Definition of the IoT device classes based on network traffic flow features”, in 4th EAI International Conference on Management of Manufacturing Systems. Springer, Cham, 2020, pp 1-17.

C. L. Stergiou, K.E. Psannis, et al. “IoT-based Big Data secure management in the Fog over a 6G Wireless Network”, IEEE Internet of Things Journal, 2020, doi: 10.1109/JIOT.2020.3033131.

P.K. Senyo, E. Addae, R. Boateng, “Cloud computing research: “A review of research themes, frameworks, methods and future research directions”, International Journal of Information Management, vol. 38, no. 1, pp 128- 139, 2018.

[8] Y. Deepthi, Kalyan K.P., Vyas M., Radhika K., Babu D.K., Krishna Rao N.V. “Disease Prediction Based on Symptoms Using Machine Learning”. In: Sikander A., Acharjee D., Chanda C., Mondal P., Verma P. (eds) Energy Systems, Drives and Automations. Lecture Notes in Electrical Engineering, vol. 664, Springer, 2020.

F. Jabeen, M. Maqsood, M. A. Ghazanfar, F. Aadil, S. Khan, K. Kim, “An IoT based efficient hybrid recommender system for cardiovascular disease,” Peer-to-Peer Networking and Applications, vol. 12, no. 5, pp 1263-1276, 2019.

D. Palani, K. Venkatalakshmi, “An IoT Based Predictive Modelling for Predicting Lung Cancer Using Fuzzy Cluster Based Segmentation and Classification,” Journal of medical systems, vol. 43, no. 2, pp. 1-12.

A. Benjemmaa, H. Lti, M. Ben Ayed, “Design of Remote Heart Monitoring System for Cardiac Patients”, In International Conference on Advanced Information Networking and Applications. Springer, Cham, 2019. pp. 963-976.

Hashi, E. K., Zaman, M. S. U., Hasan, M. R. “An expert clinical decision support system to predict disease using classification techniques”. International Conference on Electrical, Computer and Communication Engineering (ECCE), 2017. pp. 396-400.

P. P. Chavda, “Early Detection of Cardiac Disease Using Machine Learning,” In 2nd International Conference on Advances in Science & Technology (ICAST) 2019. http://dx.doi.org/10.2139/ssrn.3370813

K. Saravananathan, T. Velmurugan, “Analyzing Diabetic Data using Classification Algorithms in Data Mining,” Indian Journal of Science and Technology, vol. 9, no. 43, pp 1-6, 2016.

K.U. Sreekanth, K.P. Nitha., “A Study on Health Care in Internet of Things. International”, Journal on Recent and Innovation Trends in Computing and Communication, vol. 4, no. 2, 2016.

D. Dziak, B. Jachimczyk, W. Kulesza, “IoT-Based Information System for Healthcare Application: Design Methodology Approach,” Applied Sciences, vol. 7, no. 6, pp. 596, 2017.

L. Yehia, A. Khedr, and A. Darwish, “Hybrid Security Techniques for Internet of Things Healthcare Applications,” Advances in Internet of Things, vol. 5, no. 3, pp. 21–25, 2015.

G. Huang, K. Huang, T. Lee and J. Weng, “An interpretable rule-based diagnostic classification of diabetic nephropathy among type 2 diabetes patients”, BMC Bioinformatics, vol. 16, no. 1, pp. 1-10, 2015.

Iyer, A., Jeyalatha, S., Sumbaly, R. “Diagnosis of diabetes using classification mining techniques”. arXiv preprint arXiv:1502.03774. 2015. DOI: 10.5121/ijdkp.2015.5101.

Md. Shahriar Hassan, Atiqur Rahman, Ahmed Wasif Reza, “Health status from your body to the cloud: The behavioral relationship between IoT and classification techniques in abnormal situations,” Sensors for Health Monitoring, Elsevier 2019 Jan 1, pp. 135-156.

S. M. Riazul Islam, M. HumaunKabir, M. Hossain, Daehan Kwak, Kyung-Sup Kwak, “The Internet of Things for Health Care: A Comprehensive Survey,” IEEE Access, vol. 3, pp. 678–708, 2015.

Hashi, E. K., Zaman, M. S. U., Hasan, M. R. “An expert clinical decision support system to predict disease using classification techniques”. In 2017 International Conference on Electrical, Computer and Communication Engineering (ECCE), 2017, pp. 396-400.

P. Patidar, A. Tiwari, “Handling Missing Value in Decision Tree Algorithm,” International Journal of Computer Applications, vol. 70, no. 13, pp. 31-36, 2013.

A. Naik, L. Samant, “Correlation Review of Classification Algorithm Using Data Mining Tool: WEKA, Rapidminer, Tanagra, Orange and Knime,” Procedia Computer Science, vol. 85, pp. 662-668, 2016.

Saravananathan, K., Velmurugan, T. “Analyzing Diabetic Data using Classification Algorithms in Data Mining,” Indian Journal of Science and Technology, vol. 9, no. 43, pp. 1-6, 2016.

A.M. Koli, M. Ahmed, “Machine Learning Based Parametric Estimation Approach for Poll Prediction”, Recent Advances in Computer Science and Communications, vol. 14, no. 4, pp. 1287-1299, doi: 10.2174/266625 5813666191204112601.

Thanh Noi, P., Kappas, M.” Comparison of random forest, k-nearest neighbor, and support vector machine classifiers for land cover classification using Sentinel-2 imagery. Sensors”, vol. 18, no. 1, pp. 18, 2018.

Podgorelec V, Kokol P, Stiglic B, Rozman I. “Decision trees: an overview and their use in medicine”, Journal of medical systems, vol. 26, pp 445-463, 2002.

Elmachtoub, A., Liang, J. C. N., McNellis, R. “Decision trees for decision-making under the predict-then-optimize framework”. In International Conference on Machine Learning 2020, pp. 2858-2867.

Kumar, P. M., & Gandhi, U. D. “A novel three-tier Internet of Things architecture with machine learning algorithm for early detection of heart diseases,” Computers & Electrical Engineering, vol. 65, pp. 222-235, 2018.

Chen PT, Lin CL, Wu WN. “Big data management in healthcare: Adoption challenges and implications”, International Journal of Information Management, vol. 53, 102078, 2020.

Islam M. S., Islam M. T., Almutairi A. F., Beng G. K., Misran N., Amin N., “Monitoring of the human body signal through the Internet of Things (IoT) based LoRa wireless network system,” Applied Sciences, vol. 9, pp. 1884, 2019.

Dejene, D., Tiwari, B., Tiwari, V. “TD2SecIoT: Temporal, Data-Driven and Dynamic Network Layer Based Security Architecture for Industrial IoT,” International Journal of Interactive Multimedia and Artificial Intelligence, vol. 6, no. 4, pp. 146-156, 2020, doi: 10.9781/ijimai.2020.10.002.

Harish, B. S., Roopa, C. K. “Automated ECG Analysis for Localizing Thrombus in Culprit Artery Using Rule Based Information Fuzzy Network,” International Journal of Interactive Multimedia and Artificial Intelligence, vol. 6, no. 1, pp. 16-25, 2020, doi: 10.9781/ijimai.2019.02.001.