Е-Публикации
Технически университет - София

Abstract: Technological developments have revolutionized biomedical research and treatment, resulting in the massive collection of diverse biomedical data. Artificial intelligence techniques are widely used by medical researchers because they make it possible to identify and create models of complex datasets and the relationships between them, which in turn allows for the successful prediction of future outcomes related to a particular illness type. A methodology for medical data analysis by applying artificial intelligence techniques is proposed, which uses data to construct a desired model and address a specific problem. The methodology involves utilizing data to construct the desired model and address a particular issue. The proposed approach for healthcare data analytics applies an algorithm based on machine learning to select the most suitable model. The preprocessing phase is associated with feature extraction, data function selection, data cleaning and selection of training and validation datasets. The input dataset is transformed into numerical format. A cardiovascular data analysis workflow applying a structured approach to develop a predictive model using neural network consisting of two hidden layers is designed.

References

1. Raghupathi, W., Raghupathi, V.: Big data analytics in healthcare: promise and potential. Health Inf. Sci. Syst. 2, 3 (2014). https://doi.org/10.1186/2047-2501-2-3
2. Bahmani, A., Alavi, A., Buergel, T., et al.: A scalable, secure, and interoperable platform for deep data-driven health management. Nat. Commun. 12, 5757 (2021). https://doi.org/10.1038/s41467-021-26040-1
3. Hulsen, T., et al.: From big data to precision medicine. Front. Med. 6, 34 (2019). https://doi.org/10.3389/fmed.2019.00034
4. Avila, K., Sanmartin, P., Jabba, D., Jimeno, M.: Applications based on service-oriented architecture (SOA) in the field of home healthcare. Sensors 17(8), 1703 (2017). https://doi.org/10.3390/s17081703
5. Yang, T.H., Sun, Y.S., Lai, F.: A scalable healthcare information system based on a service-oriented architecture. J. Med. Syst. 35(3), 391–407 (2011). https://doi.org/10.1007/s10916-009-9375-5. Epub 2009 Sep 24. PMID: 20703551
6. McPadden, J., et al.: A Scalable Data Science Platform for Healthcare and Precision Medicine Research (2018). arXiv:1808.04849, https://doi.org/10.48550/arXiv.1808.04849
7. Ow, G.S., Kuznetsov, V.A.: Big genomics and clinical data analytics strategies for precision cancer prognosis. Sci. Rep. 6, 36493 (2016). https://doi.org/10.1038/srep36493
8. Ginsburg, G.S., Phillips, K.A.: Precision medicine: from science to value. Health Aff. (Millwood) 37(5), 694–701 (2018). https://doi.org/10.1377/hlthaff.2017.1624
9. Panahiazar, M., Taslimitehrani, V., Jadhav, A., Pathak, J.: Empowering personalized medicine with big data and semantic web technology: promises, challenges, and use cases. In: Proceedings of IEEE International Conference on Big Data, pp. 790–795 (2014). https://doi.org/10.1109/BigData.2014.7004307
10. Viceconti, M., Hunter, P., Hose, R.: Big data, big knowledge: big data for personalized healthcare. IEEE J. Biomed. Health Inform. 19, 1209–1215 (2015). https://doi.org/10.1109/JBHI.2015.2406883
11. Balasubramanian, V., Jolfaei, A.: A scalable framework for healthcare monitoring application using the Internet of Medical Things. Softw Pract Exp. (2020). https://doi.org/10.1002/spe.2849
12. Mallappallil, M., et al.: A review of big data and medical research. SAGE Open Med. 8, 2050312120934839 (2020). https://doi.org/10.1177/2050312120934839
13. Sidey-Gibbons, J., Sidey-Gibbons, C.: Machine learning in medicine: a practical introduction. BMC Med. Res. Methodol. 19, 64 (2019). https://doi.org/10.1186/s12874-019-0681-4
14. Davenport, T., Kalakota, R.: The potential for artificial intelligence in healthcare. Future Healthc. J. 6(2), 94–98 (2019). https://doi.org/10.7861/futurehosp.6-2-94, PMID: 31363513; PMCID: PMC6616181
15. Alowais, S.A., Alghamdi, S.S., Alsuhebany, N., et al.: Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC Med. Educ. 23, 689 (2023). https://doi.org/10.1186/s12909-023-04698-z
16. Hulsen, T.: Literature analysis of artificial intelligence in biomedicine. Ann. Transl. Med. 10(23), 1284 (2022). https://doi.org/10.21037/atm-2022-50, PMID: 36618779; PMC9816850
17. Khan, Z.F., Alotaibi, S.R.: Applications of artificial intelligence and big data analytics in m-health: a healthcare system perspective. J. Healthc. Eng. 30, 2020:8894694 (2020). https://doi.org/10.1155/2020/8894694, PMID: 32952992; PMCID: PMC7481991
18. Hlaing, K.S., Thaw, Y.M.K.K.: Applications techniques and trends of data mining and knowledge discovery database. Int. J. Trend Sci. Res. Dev. 3, 1604–1606 (2019)
19. Pushp, C.S.: Knowledge discovery and data mining for intelligent business solutions. In: Tiwari, S., Trivedi, M.C., Kolhe, M.L., Mishra, K., Singh, B.K. (eds.) Advances in Data and Information Sciences. LNNS, vol. 318. Springer, Singapore (2022). https://doi.org/10.1007/978-981-16-5689-7_18
20. Pareek, M., Bhari, P.: A review report on knowledge discovery in databases and various techniques of data mining. Open Access Int. J. Sci. Eng. 2020, 79–82 (2020)
21. Parihar A., Sharma, S.: Knowledge discovery and data mining healthcare. Int. J. Inf. Technol. Insights Transformations 4(1) (2020)
22. Borovska, P., Gancheva, V., Georgiev, I.: Platform for adaptive knowledge discovery and decision making based on big genomics data analytics. In: Rojas, I., Valenzuela, O., Rojas, F., Ortuño, F. (eds.) Bioinformatics and Biomedical Engineering. IWBBIO 2019. LNCS(), vol. 11466, pp. 297–308. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-17935-9_27
23. Sun, S., et al.: A survey of optimization methods from a machine learning perspective. IEEE Trans. Cybern. 50(8), 3668–3681 (2019)
24. Sarker I.H.: Machine learning: algorithms, real-world applications and research directions. SN Comput. Sci. 2(3), 160 (2021). https://doi.org/10.1007/s42979-021-00592-x
25. Mahesh, B.: Machine learning algorithms - a review. Int. J. Sci. Res. 9, 381–386 (2020)
26. Heart Disease Dataset. https://www.kaggle.com/datasets/johnsmith88/heart-disease-dataset. Accessed 25 Mar 2024

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