Enhanced Knowledge Based System for Cardiovascular Disease Prediction using Advanced Fuzzy TOPSIS

Authors

  • Huma Parveen Computer science and Engineering, Amity University, Uttar Pradesh, India.
  • Syed Wajahat Abbas Rizvi Computer science and Engineering, Amity University, Uttar Pradesh , India
  • Raja Sarath Kumar Boddu Computer science and Engineering, Lenora College of engineering, Rampachodavaram, Andhra Pradesh, India.

Keywords:

Cardiovascular Disease, advanced fuzzy TOPSIS, Knowledge based system, Fuzzy Logic, Disease Risk Prediction system

Abstract

Cases of cardiovascular diseases have risen over the last decade, making them the leading cause of mortality. Early detection of heart diseases helps doctors provide better treatment to patients. In this research, a knowledge-based hybrid heart disease prediction model using advanced fuzzy techniques and artificial neural networks (ANN) is proposed. The ANN and advanced fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) techniques are implemented in the proposed methodology for risk prediction of disease and disease classification, respectively. The Analytic Hierarchy Process (AHP) method's attribute weights help make effective prediction of diseases. The proposed model (ANN+ fuzzy TOPSIS) has been measured over various performance-measuring metrics and then compared to other traditional techniques to determine its efficiency. Numerical analysis of the proposed model shows that it performed better than other conventional methods in terms of accuracy (0.99), precision (0.98), specificity (0.978), F-measure (0.981), sensitivity (0.996), and many more. The goal of this research is to improve knowledge-based systems' efficacy through the application of fuzzy logic and ANN for cardiovascular disease prediction and classification.

Downloads

Download data is not yet available.

References

P. Bhatnagar et al., “The epidemiology of cardiovascular disease in the UK 2014,” Heart, vol. 101, no. 15, pp. 1182-1189, 2015 [doi:10.1136/heartjnl-2015-307516].

M. T. Maeder et al., “A clinical approach to obstructive sleep apnea as a risk factor for cardiovascular disease,” Vasc. Health Risk Manag., vol. 12, pp. 85-103, 2016 [doi:10.2147/VHRM.S74703].

R. Alugubelli, “Exploratory study of artificial intelligence in healthcare,” Int. J. Innov. Eng. Res. Technol., vol. 3, no. 1, pp. 1-10, 2016.

N. C. Long et al., “A highly accurate firefly based algorithm for heart disease prediction,” Expert Syst. Appl., vol. 42, no. 21, pp. 8221-8231, 2015 [doi:10.1016/j.eswa.2015.06.024].

Chitra and V. Seenivasagam, “Heart disease prediction system using supervised learning classifier,” BIJSESC, vol. 3, no. 1, p. 1-7, 2013 [doi:10.9756/BIJSESC.4336].

S. Jurado et al., “Hybrid methodologies for electricity load forecasting: Entropy-based feature selection with machine learning and soft computing techniques,” Energy, vol. 86, pp. 276-291, 2015 [doi:10.1016/j.energy.2015.04.039].

M. Rostami et al., “Review of swarm intelligence-based feature selection methods,” Eng. Appl. Artif. Intell., vol. 100, p. 104210, 2021 [doi:10.1016/j.engappai.2021.104210].

S. Bouktif et al., “Optimal deep learning LSTM model for electric load forecasting using feature selection and genetic algorithm: Comparison with machine learning approaches,” Energies, vol. 11, no. 7, p. 1636, 2018 [doi:10.3390/en11071636].

M. Thiyagaraj and G. Suseendran, “Survey on heart disease prediction system based on data mining techniques,” Indian J. Innov. Dev., vol. 6, no. 1, pp. 1-9, 2017.

H. B. Sola et al., “Interval type-2 fuzzy sets are generalization of interval-valued fuzzy sets: Toward a wider view on their relationship,” IEEE Trans. Fuzzy Syst., vol. 23, no. 5, pp. 1876-1882, 2014 [doi:10.1109/TFUZZ.2014.2362149].

S. A. Mokeddem, “A fuzzy classification model for myocardial infarction risk assessment,” Appl. Intell., vol. 48, no. 5, pp. 1233-1250, 2018 [doi:10.1007/s10489-017-1102-1].

P. A. Burrough and A. Frank, Geographic Objects with Indeterminate Boundaries. CRC Press, 2020.

B.-J. Wen, “A biomedical application by using optimal fuzzy sliding-mode control” in Sliding Mode Control. IntechOpen, 2011.

R. Dhanalakshmi et al., “RETRACTED ARTICLE: Association rule generation and classification with fuzzy influence rule based on information mass value,” J. Ambient Intell. Hum. Comput., vol. 12, no. 6, pp. 6613-6620, 2021 [doi:10.1007/s12652-020-02280-9].

V. Krishnaiah et al., “Heart disease prediction system using data mining techniques and intelligent fuzzy approach: A review,” Int. J. Comput. Appl., vol. 136, no. 2, pp. 43-51, 2016 [doi:10.5120/ijca2016908409].

Bashir et al., “Improving heart disease prediction using feature selection approaches” in 16th international bhurban conference on applied sciences and technology (IBCAST), vol. 2019. IEEE, 2019, pp. 619-623 [doi:10.1109/IBCAST.2019.8667106].

J. Han et al., Data Mining: Concepts and Techniques. Morgan Kaufmann, 2022.

R. O. Duda and P. E. Hart, Pattern Classification and Scene Analysis. New York: Wiley, 1973.

V. R. Elangovan et al., “Heart atherosclerosis detection using FCM+ kMeans Algorithm” in 2nd International Conference on Computation, Automation and Knowledge Management (ICCAKM), vol. 2021. IEEE, 2021, pp. 102-106 [doi:10.1109/ICCAKM50778.2021.9357719].

Segatori et al., “On distributed fuzzy decision trees for big data,” IEEE Trans. Fuzzy Syst., vol. 26, no. 1, pp. 174-192, 2017 [doi:10.1109/TFUZZ.2016.2646746].

Pamela et al., 2013, “‘A Fuzzy Optimization Technique for the Prediction of Coronary Heart Disease Using Decision Tree,’ semantic scholar”. Available at: https://www.semanticscholar.org/paper/A-Fuzzy-Optimization-Technique-for-the-Prediction-Pamela-Gayathri/aad05b5d65d39eb4ca2fa897566e6d4f619c849b.

T. Santhanam and E. P. Ephzibah, “Heart disease prediction using hybrid genetic fuzzy model,” Indian J. Sci. Technol., vol. 8, no. 9, p. 797, 2015 [doi:10.17485/ijst/2015/v8i9/52930].

P. Sharma and K. Saxena, “Application of fuzzy logic and genetic algorithm in heart disease risk level prediction,” Int. J. Syst. Assur. Eng. Manag., vol. 8, no. S2, pp. 1109-1125, 2017 [doi:10.1007/s13198-017-0578-8].

“Genetic Fuzzy Systems and Multi-Objective Evolutionary Fuzzy Systems: Taxonomy, Current Research Trends and Prospects | Soft Computing and Intelligent Information Systems. Available at: sci2s.ugr.es. Available at: https://sci2s.ugr.es/gfs.

Casalino et al., “A hierarchical fuzzy system for risk assessment of cardiovascular disease” in IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS), vol. 2020. IEEE, 2020, pp. 1-7 [doi:10.1109/EAIS48028.2020.9122750].

A. M. Sagir and S. Sathasivam, “A novel adaptive neuro fuzzy inference system based classification model for heart disease prediction,” Pertanika J. Sci. Technol., vol. 25, no. 1, 2017.

Rizvi et al., “A fuzzy inference system (FIS) to evaluate the security readiness of cloud service providers,” J. Cloud Comput., vol. 9, pp. 1-17, 2020.

W. Wójcik et al., “Medical fuzzy-expert system for assessment of the degree of anatomical lesion of coronary arteries,” Int. J. Environ. Res. Public Health, vol. 20, no. 2, p. 979, 2023 [doi:10.3390/ijerph20020979].

O. Taylan et al., “Early prediction in classification of cardiovascular diseases with machine learning, neuro-fuzzy and statistical methods,” Biology, vol. 12, no. 1, p. 117, 2023 [doi:10.3390/biology12010117].

S. Kharya et al., “Fuzzy weighted Bayesian belief network: A medical knowledge-driven Bayesian model using fuzzy weighted rules,” Int. J. Inf. Technol., vol. 15, no. 2, pp. 1117-1125, 2023 [doi:10.1007/s41870-022-01153-y].

T. Seslier and M. Ö. Karakuş, In healthcare applications of machine learning algorithms for prediction of heart attacks Journal of Scientific Reports-A, vol. 051, pp. 358-370, 2022.

R. G. Nadakinamani et al., “Clinical data analysis for prediction of cardiovascular disease using machine learning techniques,” Comp. Intell. Neurosci., vol. 2022, 2973324, 2022 [doi:10.1155/2022/2973324].

D. Cenitta et al., “Ischemic heart disease multiple imputation technique using machine learning algorithm,” Eng. Sci., vol. 19, pp. 262-272, 2022 [doi:10.30919/es8d681].

P. Doppala et al., “A reliable machine intelligence model for accurate identification of cardiovascular diseases using ensemble techniques,” J. Healthc. Eng., vol. 2022, 2585235, 2022 [doi:10.1155/2022/2585235].

R. Yilmaz and F. H. Yağin, “Early detection of coronary heart disease based on machine learning methods,” Med. Rec., vol. 4, no. 1, pp. 1-6, 2022 [doi:10.37990/medr.1011924].

S. Thukral and J. S. Bal, “Medical applications on fuzzy logic inference system: A review,” Int. J. Adv. Netw. Appl., vol. 10, no. 4, pp. 3944-3950, 2019 [doi:10.35444/IJANA.2019.10046].

T. Kasbe and R. S. Pippal, “Design of heart disease diagnosis system using fuzzy logic” in International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS), vol. 2017. IEEE, 2017, pp. 3183-3187 [doi:10.1109/ICECDS.2017.8390044].

H. Parveen et al., “Disease risk level prediction based on knowledge driven optimized deep ensemble framework,” Biomed. Signal Process. Control, vol. 79, p. 103991, 2023 [doi:10.1016/j.bspc.2022.103991].

Y. Atkov et al., “Coronary heart disease diagnosis by artificial neural networks including genetic polymorphisms and clinical parameters,” J. Cardiol., vol. 59, no. 2, pp. 190-194, 2012 [doi:10.1016/j.jjcc.2011.11.005].

Knok et al., “Implementation of intelligent model for pneumonia detection,” Tehn. Glas., vol. 13, no. 4, pp. 315-322, 2019 [doi:10.31803/tg-20191023102807].

A. H. N. Kishore and V. E. Jayanthi, “Neuro-fuzzy based medical decision support system for coronary artery disease diagnosis and risk level prediction,” J. Comp. Theor. Nanosci., vol. 15, no. 3, pp. 1027-1037, 2018 [doi:10.1166/jctn.2018.7198].

“Artificial neural network modelling,” Ebrary. Available at: https://ebrary.net/132934/engineering/artificial_neural_network_modelling.

Nădăban et al., “Fuzzy TOPSIS: A general view,” Procedia Comput. Sci., vol. 91, pp. 823-831, 2016 [doi:10.1016/j.procs.2016.07.088].

Downloads

Published

11.01.2024

How to Cite

Parveen, H. ., Rizvi, S. W. A. ., & Boddu, R. S. K. . (2024). Enhanced Knowledge Based System for Cardiovascular Disease Prediction using Advanced Fuzzy TOPSIS. International Journal of Intelligent Systems and Applications in Engineering, 12(11s), 570–583. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4478

Issue

Vol. 12 No. 11s (2024)

Section

Research Article

License

Creative Commons 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.