Volume 23 , Issue 4 , PP: 323-336, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Ahmedia Musa M. Ibrahim 1 * , Mohammed M. A. Almazah 2 , Badr Eldeen A. A. Abouzeed 3 , Murtada K. Abdalla Abdelmahmod 4
Doi: https://doi.org/10.54216/IJNS.230425
Heart disease (HD) is considered the main cause of death rate around the world. Multiple systems and biomedical instruments in hospitals take large amounts of medical data. Thus, understanding the data linked with HD is vital to enhance the prediction performance. The timely intervention of HD is the most important factor in preventing patients from additional damage. In recent times, non-invasive medical procedures, including artificial intelligence-based approaches have been used in the healthcare sector. Particularly machine learning (ML) applies various techniques and algorithms that are extensively applied and are especially effective in accurately detecting HDs within short period. However, HD prediction is a challenging task. The largest size of medicinal database has made it a challenge for clinicians to understand the complicated feature relations and make disease predictions. Therefore, this study presents a Neutrosophic Fuzzy SAW with Artificial Intelligence for Sustainable Heart Disease Recognition and Classification (NFSAW-AISHDC) technique in Healthcare Sector. The NFSAW-AISHDC technique mainly focuses on the adoption of neutrosophic fuzzy simple additive weighting (NFSAW) with feature selection process for HD detection. The NFSAW-AISHDC method exploits min-max scalar to scale the input medical data. For feature selection, the NFSAW-AISHDC method uses beluga whale optimization (BWO) algorithm to choose feature subsets. Moreover, the NFSAW-AISHDC technique applies NFSAW approach to the identification of HDs. The performance values of the NFSAW-AISHDC methodology undergoes using benchmark database. The experimental outcome underlined the promising results of the NFSAW-AISHDC method with other models.
Heart Disease Recognition , Artificial Intelligence , Beluga Whale Optimization , Neutrosophic , SAW , Feature Selection
[1] W. A. W. A. Bakar, N. L. N. B. Josdi, M. B. Man, and M. A. B. Zuhairi, ‘‘A review: Heart disease prediction in machine learning & deep learning,’’ in Proc. 19th IEEE Int. Colloq. Signal Process. Appl. (CSPA), Mar. 2023, pp. 150–155.
[2] I. S. Brites, L. M. Silva, J. L. Barbosa, S. J. Rigo, S. D. Correia, and V. R. Leithardt, ‘‘Machine learning and IoT applied to cardiovascular diseases identification through heart sounds: A literature review,’’ in Proc. Int. Conf. Inf. Technol. Syst. Cham, Switzerland: Springer, Feb. 2022, pp. 356–388.
[3] U. Nagavelli, D. Samanta, and P. Chakraborty, ‘‘Machine learning technology-based heart disease detection models,’’ J. Healthcare Eng., vol. 2022, pp. 1–9, Feb. 2022.
[4] P. Wang, Z. Lin, X. Yan, Z. Chen, M. Ding, Y. Song, and L. Meng, ‘‘A wearable ECG monitor for deep learning based real-time cardiovascular disease detection,’’ 2022, arXiv:2201.10083.
[5] C. Kim, G. Lee, H. Oh, G. Jeong, S. W. Kim, E. J. Chun, Y.-H. Kim, J.-G. Lee, and D. H. Yang, ‘‘A deep learning–based automatic analysis of cardiovascular borders on chest radiographs of valvular heart disease: Development/external validation,’’ Eur. Radiol., vol. 32, no. 3, pp. 1558–1569, Mar. 2022.
[6] B. Kolukisa and B. Bakir-Gungor, ‘‘Ensemble feature selection and classification methods for machine learning-based coronary artery disease diagnosis,’’ Comput. Standards Interfaces, vol. 84, Mar. 2023, Art. no. 103706.
[7] M. Ganesan and N. Sivakumar, ‘‘IoT based heart disease prediction and diagnosis model for healthcare using machine learning models,’’ in Proc. IEEE Int. Conf. Syst., Comput., Autom. Netw. (ICSCAN), Mar. 2019, pp. 1–5.
[8] J. P. Li, A. U. Haq, S. U. Din, J. Khan, A. Khan, and A. Saboor, ‘‘Heart disease identification method using machine learning classification in e-healthcare,’’ IEEE Access, vol. 8, pp. 107562–107582, 2020.
[9] R. Atallah and A. Al-Mousa, ‘‘Heart disease detection using machine learning majority voting ensemble method,’’ in Proc. 2nd Int. Conf. New Trends Comput. Sci. (ICTCS), Oct. 2019, pp. 1–6.
[10] P. K. Shrivastava, M. Sharma, P. Sharma, and A. Kumar, ‘‘HCBiLSTM: A hybrid model for predicting heart disease using CNN and BiLSTM algorithms,’’ Meas., Sensors, vol. 25, Feb. 2023, Art. no. 100657.
[11] Sheela, A.J. and Krishnamurthy, M., 2024. Revolutionizing cardiovascular risk prediction: A novel image-based approach using fundus analysis and deep learning. Biomedical Signal Processing and Control, 90, p.105781.
[12] Papandrianos, N.I., Feleki, A., Papageorgiou, E.I. and Martini, C., 2022. Deep learning-based automated diagnosis for coronary artery disease using SPECT-MPI images. Journal of Clinical Medicine, 11(13), p.3918.
[13] Obayya, M., Alsamri, J.M., Al-Hagery, M.A., Mohammed, A. and Hamza, M.A., 2023. Automated Cardiovascular Disease Diagnosis Using Honey Badger Optimization with Modified Deep Learning Model. IEEE Access.
[14] Venkatesh, C., Prasad, B.V.V.S., Khan, M., Babu, J.C. and Dasu, M.V., 2024. An automatic diagnostic model for the detection and classification of cardiovascular diseases based on swarm intelligence technique. Heliyon.
[15] Ahmad, S., Asghar, M.Z., Alotaibi, F.M. and Alotaibi, Y.D., 2023. Diagnosis of cardiovascular disease using deep learning technique. Soft Computing, 27(13), pp.8971-8990.
[16] Bekheet, M., Sallah, M., Alghamdi, N.S., Rusu-Both, R., Elgarayhi, A. and Elmogy, M., 2024. Cardiac Fibrosis Automated Diagnosis Based on FibrosisNet Network Using CMR Ischemic Cardiomyopathy. Diagnostics, 14(3), p.255.
[17] Khanna, A., Selvaraj, P., Gupta, D., Sheikh, T.H., Pareek, P.K. and Shankar, V., 2023. Internet of things and deep learning enabled healthcare disease diagnosis using biomedical electrocardiogram signals. Expert Systems, 40(4), p.e12864.
[18] Shantal, M., Othman, Z. and Bakar, A.A., 2023. A Novel Approach for Data Feature Weighting Using Correlation Coefficients and Min–Max Normalization. Symmetry, 15(12), p.2185.
[19] Yuan, H., Chen, Q., Li, H., Zeng, D., Wu, T., Wang, Y. and Zhang, W., 2024. Improved beluga whale optimization algorithm based cluster routing in wireless sensor networks. Mathematical Biosciences and Engineering, 21(3), pp.4587-4625.
[20] Ajay, D., Manivel, M. and Aldring, J., 2019. Neutrosophic Fuzzy SAW Method and It’s Application. The International journal of analytical and experimental modal analysis, 11(8), pp.881-887.
[21] Sevastjanov, P., Dymova, L. and Kaczmarek, K., 2021. On the neutrosophic, pythagorean and some other novel fuzzy sets theories used in decision making: invitation to discuss. Entropy, 23(11), p.1485.
[22] https://www.kaggle.com/datasets/sid321axn/heart-statlog-cleveland-hungary-final
