Deep Learning Enabled Whale Optimization Algorithm for Accurate Prediction of RA Disease

Authors

  • K. Prabavathy Department of Data Science and Analytics, Sree Saraswathi Thyagaraja College, Pollachi.India Author
  • M. Nalini Department of Computer Science, Rathinam College of Arts and Science, Coimbatore.India Author

DOI:

https://doi.org/10.56294/sctconf2024652

Keywords:

Whale Optimization Algorithm, Deep Neural Network, Rheumatoid Arthritis, Accurate Prediction

Abstract

Whale Optimization Algorithm (WOA) is an optimization technique and based on food foraging behavior of whales. It has been applied in many domain including processing of images, framework controls, and ML (machine learning). WOA assists in choosing the right parameters required for Deep Neural Networks. This work uses DNN to examine metacarpophalangeal (MCP) rheumatoid joint discomforts in patients from diagnostic medical images including X-rays or Magnetic Resource images. The use of WOA enhances resultant outcomes of DNN as it searched for optimal solutions within search spaces, instead of getting trapped in local minima found by gradient descent. The combination of WOA and DNN for grading MCP rheumatoid arthritis can provide an efficient and accurate solution for medical practitioners and researchers

References

1. Ahalya, R.K., Umapathy, S., Krishnan, P.T. and Joseph Raj, A.N., 2022. Automated evaluation of rheumatoid arthritis from hand radiographs using Machine Learning and deep learning techniques. Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine, 236(8), pp.1238-1249.

2. Almutairi, K., Nossent, J., Preen, D., Keen, H. and Inderjeeth, C., 2021. The global prevalence of rheumatoid arthritis: a meta-analysis based on a systematic review. Rheumatology international, 41(5), pp.863-877.

3. Amado DPA, Diaz FAC, Pantoja R del PC, Sanchez LMB. Benefits of Artificial Intelligence and its Innovation in Organizations. AG Multidisciplinar 2023;1:15-15. https://doi.org/10.62486/agmu202315.

4. Batista-Mariño Y, Gutiérrez-Cristo HG, Díaz-Vidal M, Peña-Marrero Y, Mulet-Labrada S, Díaz LE-R. Behavior of stomatological emergencies of dental origin. Mario Pozo Ochoa Stomatology Clinic. 2022-2023. AG Odontologia 2023;1:6-6. https://doi.org/10.62486/agodonto20236.

5. Battafarano DF, Ditmyer M, Bolster MB, et al.American College of Rheumatology Workforce Study: Supply and Demand Projections of Adult Rheumatology Workforce, Arthritis Care Res (Hoboken) 2018;70:617–26.

6. Caero L, Libertelli J. Relationship between Vigorexia, steroid use, and recreational bodybuilding practice and the effects of the closure of training centers due to the Covid-19 pandemic in young people in Argentina. AG Salud 2023;1:18-18. https://doi.org/10.62486/agsalud202318.

7. Cavalcante L de FB. Feminicide from the perspective of the cultural mediation of information. Advanced Notes in Information Science 2023;5:24-48. https://doi.org/10.47909/978-9916-9906-9-8.72.

8. Chalan SAL, Hinojosa BLA, Claudio BAM, Mendoza OAV. Quality of service and customer satisfaction in the beauty industry in the district of Los Olivos. SCT Proceedings in Interdisciplinary Insights and Innovations 2023;1:5-5. https://doi.org/10.56294/piii20235.

9. Chávez JJB, Trujillo REO, Hinojosa BLA, Claudio BAM, Mendoza OAV. Influencer marketing and the buying decision of generation «Z» consumers in beauty and personal care companies. SCT Proceedings in Interdisciplinary Insights and Innovations 2023;1:7-7. https://doi.org/10.56294/piii20237.

10. Dalal N,Triggs B, IEEE Computer Society Conference on Computer Vision and Pattern Recognition San Diego, USA.2005: 886. 10.1109/CVPR.2005.177

11. Diaz DPM. Staff turnover in companies. AG Managment 2023;1:16-16. https://doi.org/10.62486/agma202316.

12. Espinosa JCG, Sánchez LML, Pereira MAF. Benefits of Artificial Intelligence in human talent management. AG Multidisciplinar 2023;1:14-14. https://doi.org/10.62486/agmu202314.

13. Figueredo-Rigores A, Blanco-Romero L, Llevat-Romero D. Systemic view of periodontal diseases. AG Odontologia 2023;1:14-14. https://doi.org/10.62486/agodonto202314.

14. Gonzalez-Argote J, Castillo-González W. Productivity and Impact of the Scientific Production on Human-Computer Interaction in Scopus from 2018 to 2022. AG Multidisciplinar 2023;1:10-10. https://doi.org/10.62486/agmu202310.

15. Gul HL, Eugenio G, Rabin T, et al. Defining remission in rheumatoid arthritis: does it matter to the patient? A comparison of multi-dimensional remission criteria and patient reported outcomes. Rheumatology (Oxford) 2019.

16. Hernández-Flórez N. Breaking stereotypes: “a philosophical reflection on women criminals from a gender perspective". AG Salud 2023;1:17-17. https://doi.org/10.62486/agsalud202317.

17. Hinojosa BLA, Mendoza OAV. Perceptions on the use of Digital Marketing of the micro-entrepreneurs of the textile sector of the Blue Gallery in the emporium of Gamarra. SCT Proceedings in Interdisciplinary Insights and Innovations 2023;1:9-9. https://doi.org/10.56294/piii20239.

18. Hügle, M., Omoumi, P., van Laar, J.M., Boedecker, J. and Hügle, T., 2020. Applied machine learning and artificial intelligence in rheumatology. Rheumatology advances in practice, 4(1), p.rkaa005.

19. Khanna, N.N., Jamthikar, A.D., Gupta, D., Piga, M., Saba, L., Carcassi, C., Giannopoulos, A.A., Nicolaides, A., Laird, J.R., Suri, H.S. and Mavrogeni, S., 2019. Rheumatoid arthritis: atherosclerosis imaging and cardiovascular risk assessment using machine and deep learning–based tissue characterization. Current atherosclerosis reports, 21, pp.1-14.

20. Krizhevsky A,Sutskever I, Hinton G E. Advances in Neural Information Processing Systems 25 Lake Tahoe, USA.2012:1097. 10.1145/3065386

21. KS, A.S.D.M.D., Selvakumar, M., Sathyamangalam, E. and Nadu, T., 2023. Classification of Deep Learning Algorithm for Rheumatoid Arthritis Predictor.

22. Lamorú-Pardo AM, Álvarez-Romero Y, Rubio-Díaz D, González-Alvarez A, Pérez-Roque L, Vargas-Labrada LS. Dental caries, nutritional status and oral hygiene in schoolchildren, La Demajagua, 2022. AG Odontologia 2023;1:8-8. https://doi.org/10.62486/agodonto20238.

23. LeCun Y,Bengio Y, Hinton G.Nature. 2015; 521: 436

24. LeCun Y,Boser B,Denker J S, Henderson D, Howard R E Hubbard W,Jackel L D. Neural Comput. 1989; 1: 541

25. Ledesma-Céspedes N, Leyva-Samue L, Barrios-Ledesma L. Use of radiographs in endodontic treatments in pregnant women. AG Odontologia 2023;1:3-3. https://doi.org/10.62486/agodonto20233.

26. Lee S, Choi M, Choi H S, Park M S, Yoon S. IEEE Biomedical Circuits and Systems Conference Atlanta, USA. 2015 10.1109/BioCAS.2015.7348440

27. Litjens G,Kooi T,Bejnordi B E,Setio AAA,Ciompi F Ghafoorian M,Laak J A W M V D,Ginneken B V, Sánchez C I, Med. Image Anal.2017; 42: 60

28. Lopez ACA. Contributions of John Calvin to education. A systematic review. AG Multidisciplinar 2023;1:11-11. https://doi.org/10.62486/agmu202311.

29. Lowe D G. Int. J. Comput. Vis.2004; 60: 91

30. Manova M, Savova A, Vasileva M, et al. Comparative Price Analysis of Biological Products for Treatment of Rheumatoid Arthritis. Front Pharmacol.2018;9:1070.

31. Marcillí MI, Fernández AP, Marsillí YI, Drullet DI, Isalgué RF. Older adult victims of violence. Satisfaction with health services in primary care. SCT Proceedings in Interdisciplinary Insights and Innovations 2023;1:12-12. https://doi.org/10.56294/piii202312.

32. Marcillí MI, Fernández AP, Marsillí YI, Drullet DI, Isalgué VMF. Characterization of legal drug use in older adult caregivers who are victims of violence. SCT Proceedings in Interdisciplinary Insights and Innovations 2023;1:13-13. https://doi.org/10.56294/piii202313.

33. Moraes IB. Critical Analysis of Health Indicators in Primary Health Care: A Brazilian Perspective. AG Salud 2023;1:28-28. https://doi.org/10.62486/agsalud202328.

34. Murakami S Hatano K Tan J Kim H Aoki T Multimed. Tools Appl.2018 ;77:10921.

35. Ogolodom MP, Ochong AD, Egop EB, Jeremiah CU, Madume AK, Nyenke CU, et al. Knowledge and perception of healthcare workers towards the adoption of artificial intelligence in healthcare service delivery in Nigeria. AG Salud 2023;1:16-16. https://doi.org/10.62486/agsalud202316.

36. Ojha, S., Anand, S. and Kanisha, B., 2023, May. Prediction of Rheumatoid Arthritis using Deep Learning Techniques. In 2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC) (pp. 357-362). IEEE.

37. Pandit, A. and Radstake, T.R., 2020. Machine learning in rheumatology approaches the clinic. Nature Reviews Rheumatology, 16(2), pp.69-70.

38. Peñaloza JEG, Bermúdez L marcela A, Calderón YMA. Perception of representativeness of the Assembly of Huila 2020-2023. AG Multidisciplinar 2023;1:13-13. https://doi.org/10.62486/agmu202313.

39. Pérez DQ, Palomo IQ, Santana YL, Rodríguez AC, Piñera YP. Predictive value of the neutrophil-lymphocyte index as a predictor of severity and death in patients treated for COVID-19. SCT Proceedings in Interdisciplinary Insights and Innovations 2023;1:14-14. https://doi.org/10.56294/piii202314.

40. Prado JMK do, Sena PMB. Information science based on FEBAB’s census of Brazilian library science: postgraduate data. Advanced Notes in Information Science 2023;5:1-23. https://doi.org/10.47909/978-9916-9906-9-8.73.

41. Pupo-Martínez Y, Dalmau-Ramírez E, Meriño-Collazo L, Céspedes-Proenza I, Cruz-Sánchez A, Blanco-Romero L. Occlusal changes in primary dentition after treatment of dental interferences. AG Odontologia 2023;1:10-10. https://doi.org/10.62486/agodonto202310.

42. Quiroz FJR, Oncoy AWE. Resilience and life satisfaction in migrant university students residing in Lima. AG Salud 2023;1:9-9. https://doi.org/10.62486/agsalud20239.

43. Radu, A.F. and Bungau, S.G., 2021. Management of rheumatoid arthritis: an overview. Cells, 10(11), p.2857.

44. Roa BAV, Ortiz MAC, Cano CAG. Analysis of the simple tax regime in Colombia, case of night traders in the city of Florencia, Caquetá. AG Managment 2023;1:14-14. https://doi.org/10.62486/agma202314.

45. Rodríguez AL. Analysis of associative entrepreneurship as a territorial strategy in the municipality of Mesetas, Meta. AG Managment 2023;1:15-15. https://doi.org/10.62486/agma202315.

46. Rodríguez LPM, Sánchez PAS. Social appropriation of knowledge applying the knowledge management methodology. Case study: San Miguel de Sema, Boyacá. AG Managment 2023;1:13-13. https://doi.org/10.62486/agma202313.

47. Rosa JE, Garcia MV, Luissi A, et al. Rheumatoid Arthritis Patient’s Journey: Delay in Diagnosis and Treatment. J Clin Rheumatol 2019.

48. Schmidhuberj. Neural Netw.2015; 61: 85

49. Sermanet P, Eigen D, Zhang X, Mathieu M, Fergus R Lecun Y. 2013Pavlidis T,Liow Y T. IEEE Trans. Pattern Anal. Mach. Intell.1988; 12: 208

50. Serra S, Revez J. As bibliotecas públicas na inclusão social de migrantes forçados na Área Metropolitana de Lisboa. Advanced Notes in Information Science 2023;5:49-99. https://doi.org/10.47909/978-9916-9906-9-8.50.

51. Smolen JS, Aletaha D, Barton A, et al. Rheumatoid arthritis. Nat Rev Dis Primers 2018;4:18001.

52. Solano AVC, Arboleda LDC, García CCC, Dominguez CDC. Benefits of artificial intelligence in companies. AG Managment 2023;1:17-17. https://doi.org/10.62486/agma202317.

53. Sundaramurthy, S., Saravanabhavan, C. and Kshirsagar, P., 2020, November. Prediction and classification of rheumatoid arthritis using ensemble machine learning approaches. In 2020 International Conference on Decision Aid Sciences and Application (DASA) (pp. 17-21). IEEE

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Published

2024-03-11

How to Cite

1.
Prabavathy K, Nalini M. Deep Learning Enabled Whale Optimization Algorithm for Accurate Prediction of RA Disease. Salud, Ciencia y Tecnología - Serie de Conferencias [Internet]. 2024 Mar. 11 [cited 2024 Nov. 21];3:652. Available from: https://conferencias.ageditor.ar/index.php/sctconf/article/view/1066