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Fuzzy rule-based intelligent cardiovascular disease prediction using complex event processing

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Abstract

Cardiovascular disease (CVDs) is a rapidly rising global concern due to unhealthy diets, lack of physical activity, and other factors. According to the World Health Organization (WHO), primary risk factors include elevated blood pressure, glucose, blood lipids, and obesity. Recent research has focused on accurate and timely disease prediction to reduce risk and fatalities, often relying on predictive models trained on large datasets, which require intensive training. An intelligent system for CVDs patients could greatly assist in making informed decisions by effectively analyzing health parameters. CEP has emerged as a valuable method for solving real-time challenges by aggregating patterns of interest and their causes and effects on end users. In this work, a fuzzy rule-based system is proposed for monitoring clinical data to provide real-time decision support. A fuzzy rule based on clinical and WHO standards ensures accurate predictions. The integrated approach uses Apache Kafka and Spark for data streaming, and the Siddhi CEP Engine for event processing. Additionally, numerous cardiovascular disease-related parameters are passed through CEP Engine to ensure fast and reliable prediction decisions. To validate the effectiveness of the approach, simulation is done with real-time, unseen data to predict cardiovascular disease. Using synthetic data (1000 samples), and categorized it into "Very Low Risk, Low Risk, Medium Risk, High Risk, and Very High Risk." Validation results showed that 20% of samples were categorized as very low risk, 15–45% as low risk, 35–65% as medium risk, 55–85% as high risk, and 75% as very high risk.

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Data availability

No datasets were generated or analyzed during the current study.

Notes

  1. https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds)?gad_source=1&gclid=CjwKCAjwt-OwBhBnEiwAgwzrUpeh5SoPVI3sMvMeymxzCvKPDhN8MFGerLuRwP-HK4Nx6vf3t2N1ABoCvWcQAvD_BwE..

  2. https://iris.who.int/bitstream/handle/10665/43787/9789241547260_eng.pdf.

  3. https://www.acc.org//-/media/Non-Clinical/Files-PDFs-Excel-MS-Word-etc/2023/05/I23115-Advancing-CV-Health-in-India-Roundtable-Report.pdf..

  4. https://www.acpicr.com/data/Page_Downloads/ACPICR2023StandardsReaderlayout.pdf.

  5. https://www.kaggle.com/datasets/sulianova/cardiovascular-disease-dataset.

Abbreviations

CVDs:

Cardiovascular disease

WHO:

World health organization

CEP:

Complex event processing

NCDs:

Non-communicable diseases

AUROC:

Area under the ROC curve

LSTM:

Long short-term memory

CNN:

Convolutional neural network

ECG:

Electrocardiogram

FCL:

Fuzzy control language

DBP:

Diastolic blood pressure

RNN:

Recurrent neural network

SBP:

Systolic blood pressure

ACC:

American college of cardiology

MAE:

Mean absolute error

MSE:

Mean squared error

RMSE:

Root mean squared error

References

  1. Naseri MM, Tabibian S, Homayounvala E (2022) Adaptive and personalized user behavior modeling in complex event processing platforms for remote health monitoring systems. Artif Intell Med 134:102421. https://doi.org/10.1016/j.artmed.2022.102421

    Article  Google Scholar 

  2. Kumar SS, Agarwal S (2024) Rule based complex event processing for IoT applications: review, classification and challenges. Expert Syst. https://doi.org/10.1111/exsy.13597

    Article  MATH  Google Scholar 

  3. Sun AY, Zhong Z, Jeong H, Yang Q (2019) Building complex event processing capability for intelligent environmental monitoring. Environ Model Softw 116:1–6. https://doi.org/10.1016/j.envsoft.2019.02.015

    Article  MATH  Google Scholar 

  4. Hassan F, Shaheen ME, Sahal R (2020) Real-time healthcare monitoring system using online machine learning and spark streaming. Int J Adv Comput Sci Appl. https://doi.org/10.14569/IJACSA.2020.0110977

    Article  MATH  Google Scholar 

  5. Tapia E, Lopez-Novoa U, Sastoque-Pinilla L, López-de-Lacalle LN (2024) Implementation of a scalable platform for real-time monitoring of machine tools. Comput Ind 155:104065. https://doi.org/10.1016/j.compind.2023.104065

    Article  MATH  Google Scholar 

  6. Saeed N, Malik H, Naeem A, Bashir U (2024) Incorporating big data and IoT in intelligent ecosystems: state-of-the-arts, challenges and opportunities, and future directions. Multimed Tools Appl 83(7):20699–20741. https://doi.org/10.1007/s11042-023-16328-3

    Article  Google Scholar 

  7. Sardar TH, Pandey BK (eds) (2024) Big data computing: advances in technologies, methodologies, and applications. CRC Press, Boca Raton

    MATH  Google Scholar 

  8. Naseri MM, Tabibian S and Homayounvala E (2021) Intelligent rule extraction in a complex event processing platform for health monitoring systems. In: 2021 11th International Conference on Computer Engineering and Knowledge (ICCKE), pp 163–168. IEEE. https://doi.org/10.1109/ICCKE54056.2021.9721525

  9. Wang LX, Mendel JM (1992) Generating fuzzy rules by learning from examples. IEEE Trans Syst Man Cybern 22(6):1414–1427. https://doi.org/10.1109/21.199466

    Article  MathSciNet  MATH  Google Scholar 

  10. Rahman MZU, Akbar MA, Leiva V, Martin-Barreiro C, Imran M, Riaz MT, Castro C (2024) An IoT-fuzzy intelligent approach for holistic management of COVID-19 patients. Heliyon. https://doi.org/10.1016/j.heliyon.2023.e22454

    Article  Google Scholar 

  11. Hsu W, Warren J, Riddle P (2022) Multivariate sequential analytics for cardiovascular disease event prediction. Methods Inf Med 61(S02):e149–e171. https://doi.org/10.1055/s-0042-1758687

    Article  MATH  Google Scholar 

  12. Rumsfeld JS, Joynt KE, Maddox TM (2016) Big data analytics to improve cardiovascular care: promise and challenges. Nat Rev Cardiol 13(6):350–359. https://doi.org/10.1038/nrcardio.2016.42

    Article  Google Scholar 

  13. Rahmani AM, Babaei Z, Souri A (2021) Event-driven IoT architecture for data analysis of reliable healthcare applications using complex event processing. Clust Comput 24(2):1347–1360. https://doi.org/10.1007/s10586-020-03189-w

    Article  Google Scholar 

  14. Terrada O, Raihani A, Bouattane O and Cherradi B (2018). Fuzzy cardiovascular diagnosis system using clinical data. In: 2018 4th International Conference on Optimization and Applications (ICOA), pp 1–4. IEEE. https://doi.org/10.1109/ICOA.2018.8370549

  15. Ma CY, Luo YM, Zhang TY, Hao YD, Xie XQ, Liu XW, Lin H (2024) Predicting coronary heart disease in Chinese diabetics using machine learning. Comput Biol Med 169:107952. https://doi.org/10.1016/j.compbiomed.2024.107952

    Article  Google Scholar 

  16. Akili S, Purtzel S and Weidlich M (2024) DecoPa: query decomposition for parallel complex event processing. In: Proceedings of the ACM on management of data, 2(3), 1-26

  17. Chandra R, Tiwari S, Rastogi S and Agarwal S (2023) A decision support system for liver diseases prediction: integrating batch processing, rule-based event detection and SPARQL query. arXiv preprint arXiv:2311.07595. https://doi.org/10.48550/arXiv.2311.07595

  18. Li Y, Luo JH, Dai QY, Eshraghian JK, Ling BWK, Zheng CY, Wang XL (2023) A deep learning approach to cardiovascular disease classification using empirical mode decomposition for ECG feature extraction. Biomed Signal Process Control 79:104188. https://doi.org/10.1016/j.bspc.2022.104188

    Article  Google Scholar 

  19. Serdaroglu KC, Baydere S, Saovapakhiran B, Charnsripinyo C (2024) Q-IoT: QoS aware multi-layer service architecture for multi-class IoT data traffic management. IEEE Internet Things J. https://doi.org/10.1109/JIOT.2024.3402382

    Article  Google Scholar 

  20. Kumar SS, Chandra R, Agarwal S (2024) A real-time approach for smart building operations prediction using rule-based complex event processing and SPARQL query. J Supercomput. https://doi.org/10.1007/s11227-024-06276-6

    Article  MATH  Google Scholar 

  21. Kumar SS, Kumar A, Chandra R, Agarwal S, Syafrullah M and Adiyarta K (2023) Rule extraction using machine learning classifiers for complex event processing. In: 2023 10th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), pp 355–360. IEEE. https://doi.org/10.1109/EECSI59885.2023.10295840

  22. Tun MT, Nyaung DE and Phyu MP (2019) Performance evaluation of intrusion detection streaming transactions using Apache Kafka and Spark streaming. In: 2019 International Conference on Advanced Information Technologies (ICAIT), pp 25–30. IEEE. https://doi.org/10.1109/AITC.2019.8920960

  23. Ali ML, Sadi MS, Goni MO (2024) Diagnosis of heart diseases: a fuzzy-logic-based approach. PLoS ONE 19(2):e0293112. https://doi.org/10.1371/journal.pone.0293112

    Article  MATH  Google Scholar 

  24. D'silva GM, Khan A and Bari S (2017) Real-time processing of IoT events with historic data using Apache Kafka and Apache Spark with dashing framework. In: 2017 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), pp 1804–1809. IEEE. https://doi.org/10.1007/s10586-024-04434-2

  25. de Oliveira HR, Vieira AW, Santos LI, Ekel PY, Dangelo MFS (2024) A fuzzy transformation approach to enhance active learning for heart disease prediction. J Intell Fuzzy Syst. https://doi.org/10.3233/JIFS-237047

    Article  Google Scholar 

  26. Dichpally T, Wutla Y, Uday V, Midigudla RS (2024) Feature extraction and diagnosis of heart diseases using fuzzy-based IoMT. Adv Fuzzy Based Internet Med Things (IoMT). https://doi.org/10.1002/9781394242252.ch13

    Article  Google Scholar 

  27. Mirzakhanov VE (2024) Fuzzy logic in association rule mining: limited effectiveness analysis. J Exp Theor Artif Intell. https://doi.org/10.1080/0952813X.2023.2301377

    Article  Google Scholar 

  28. Kumar SS, Chandra R, Agarwal S (2024) Rule based complex event processing for an air quality monitoring system in smart city. Sustain Cities Soc 112:105609. https://doi.org/10.1016/j.scs.2024.105609

    Article  MATH  Google Scholar 

  29. Chandra R, Kumar SS, Patra R and Agarwal S (2024) Decision support system for forest fire management using ontology with big data and LLMs. arXiv preprint arXiv:2405.11346. https://doi.org/10.48550/arXiv.2405.11346

  30. Manley K, Nyelele C, Egoh BN (2022) A review of machine learning and big data applications in addressing ecosystem service research gaps. Ecosyst Serv 57:101478. https://doi.org/10.1016/j.ecoser.2022.101478

    Article  Google Scholar 

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Acknowledgements

All the authors are thankful to Big Data Analytics Lab and Support Development Center TEAL 2.O at IIIT Allahabad for providing necessary resources for conducting this research.

Funding

This research work received no funding.

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Authors and Affiliations

  1. Department of Information Technology, India Institute of Information Technology Allahabad, Prayagraj, India

    Shashi Shekhar Kumar, Ritesh Chandra, Anurag Harsh & Sonali Agarwal

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  1. Shashi Shekhar Kumar
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  2. Ritesh Chandra
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Contributions

Authorship credit statement: Shashi shekhar kumar contributed to formal analysis, conceptualization, and drafting—original manuscript. Anurag harsh contributed to coding and implementation. Ritesh chandra contributed to reviewing, conceptualization and editing—original manuscript. Sonali Agarwal contributed to supervision throughout the work.

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Correspondence to Shashi Shekhar Kumar.

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Kumar, S.S., Chandra, R., Harsh, A. et al. Fuzzy rule-based intelligent cardiovascular disease prediction using complex event processing. J Supercomput 81, 402 (2025). https://doi.org/10.1007/s11227-024-06911-2

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