Biomeditsina va amaliyot jurnali, 2022 №3

Subject of the article

ARTIFICIAL INTELLIGENCE AS THE BASIS OF DIGITAL DIABETES THERAPY (335-346)

Authors

ODILOVA Fotima Tuychiyena, ALIXANOVA Nodira Mirshovkatovna, DAVRONOV Rifqat Rahimovich, TAXIROVA Feruza Abrarovna

Institution

V.I.Romanovskiy nomidagi matematika instituti, Akademik Y.X.Turaqulov nomidagi RIEIATM,

Abstract

Raqamlashtirish asrida axborot texnologiyalarining zamonaviy usullaridan foydalanish juda muhim. Agarda ilgari texnologiyalar matematik modellashtirishga asoslangan bo'lsa, hozirda qaror qabul qilishda sun'iy intellekt keng qo'llaniladi. Maqolada kasalliklarni, bu holda qandli diabetni davolashning zamonaviy usuli sifatida "raqamli terapiya" haqida ma'lumotlar keltirilgan.

Key words

qandli diabet, sun'iy intellekt, raqamli terapiya

Literature

1. International Diabetes Federation (IDF). IDF diabetes atlas. 9th ed Brussels, Belgium: International Diabetes Federation; 2019. Available at: http://www.diabetesatlas.org [Accessed on December 27, 2019. 2. Cho NH, Shaw JE, Karuranga S, et al. IDF diabetes atlas: global estimates of diabetes prevalence for 2017 and projections for 2045. Diabetes Res Clin Pract 2018;138:271–81. https://doi.org/10.1016/j.diabres.2018.02.023. 3. Global Burden of Disease Cancer Collaboration, Fitzmaurice C, Allen C, et al. Global, regional, and national cancer incidence, mortality, years of life lost, years lived with disability, and disability-adjusted life-years for 32 cancer groups, 1990 to 2015: a systematic analysis for the global burden of disease study. JAMA Oncol 2017;3(4):524–48. https://doi.org/10.1001/jamaoncol.2016.5688. 4. Papatheodorou K, Papanas N, Banach M, Papazoglou D, Edmonds M. Complications of diabetes 2016. J Diabetes Res 2016; 2016:6989453. 5. Grzybowski A, Brona P, Lim G, et al. Artificial intelligence for diabetic retinopathy screening: a review. Eye (Lond) 2020;34(3):451–60. https://doi.org/10.1038/s41433-019-0566-0. 6. Keel S, Lee PY, Scheetz J, et al. Feasibility and patient acceptability of a novel artificial intelligence-based screening model for diabetic retinopathy at endocrinology outpatient services: a pilot study. Sci Rep 2018; 8:4330. 7. Lam C, Yu C, Huang L, Rubin D. Retinal lesion detection with deep learning using image patches. Invest Ophthalmol Vis Sci 2018; 59:590–6. 8. Nagaraj SB, Sidorenkov G, van Boven JFM, Denig P. Predicting short- and long-term glycated haemoglobin response after insulin initiation in patients with type 2 diabetes mellitus using machine-learning algorithms. Diabetes Obes Metab 2019;21(12):2704–11. https://doi.org/10.1111/dom.13860 9. Lo-Ciganic WH, Donohue JM, Thorpe JM, et al. Using machine learning to examine medication adherence thresholds and risk of hospitalization. Med Care 2015;53:720–8. 10. Zou Q, Qu K, Luo Y, Yin D, Ju Y, Tang H. Predicting diabetes mellitus with machine learning techniques. Front Genet 2018;9:515. https://doi.org/10.3389/fgene.2018.00515 11. Cichosz SL, Johansen MD, Hejlesen O. Toward big data analytics: review of predictive models in management of diabetes and its complications. J Diabetes Sci Technol 2016;10(1):27–34. 12. Yap MH, Chatwin KE, Ng CC, et al. A new mobile application for standardizing diabetic foot images. J Diabetes Sci Technol 2018;12:169–73. 13. Allalou A, Nalla A, Prentice KJ, et al. A predictive metabolic signature for the transition from gestational diabetes mellitus to type 2 diabetes. Diabetes 2016;65(9):2529–39. https://doi.org/10.2337/db15-1720 14. Seyhan AA, Carini C. Are innovation and new technologies in precision medicine paving a new era in patients centric care? J Transl Med 2019;17(1):114. 15. Han W, Ye Y. A repository of microbial marker genes related to human health and diseases for host phenotype prediction using microbiome data. Pac Symp Biocomput 2019;24:236–47. 16. Mahajan A, Taliun D, Thurner M, et al. Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps. Nature Genetics 2018;50:1505–13. 17. Wesolowska-Andersen A, Zhuo Yu G, Nylander V, et al. Deep learning models predict regulatory variants in pancreatic islets and refine type 2 diabetes association signals. Elife 2020;9:e51503. 18. Cafazzo JA, Casselman M, Hamming N, Katzman DK, Palmert MR. Design of an mHealth app for the self-management of adolescent type 1 diabetes: a pilot study. J Med Internet Res 2012;14(3):e70. https://doi.org/10.2196/jmir.2058. 19. Samer Ellahham, MD, Artificial Intelligence: The Future for Diabetes Care The American Journal of Medicine, (2020) Vol 133, No 8, 895−900 https://doi.org/10.1016/j.amjmed.2020.03.033 20. Yu W, Liu T, Valdez R et al (2010) Application of support vector machine modeling for prediction of common diseases: the case of diabetes and pre-diabetes. BMC Med Inf Decis Mak. https://doi.org/10.1186/1472-6947-10-16 21. Khalilia M, Chakraborty S, Popescu M (2011) Predicting disease risks from highly imbalanced data using random forest. BMC Med Inf Decis Mak. https://doi.org/10.1186/ 1472-6947-11-51 22. Patel PB, Shah PP, Patel HD (2017) Analyze data mining algorithms for prediction of diabetes. Comput Eng 5:466–473 23. Wu H, Yang S, Huang Z et al (2018) Type 2 diabetes mellitus prediction model based on data mining. Inf Med Unlocked 10:100–107. https://doi.org/10.1016/j.imu.2017.12. 006 24. Hina S, Shaikh A, Sattar SA (2017) Analyzing diabetes datasets using data mining. J Basic Appl Sci 13:466–471 25. Larabi-Marie-Sainte S, Aburahmah L, Almohaini R, Saba T (2019) Current techniques for diabetes prediction: review and case study. Appl Sci. https:// doi. org/ 10. 3390/ app92 14604 26. Jakka A, Rani JV (2019) Performance evaluation of machine learning models for diabetes prediction. Int J Innov Technol Explor Eng 8:1976–1980. https:// doi. org/ 10. 35940/ ijitee. K2155.09811 19 27. Kandhasamy JP, Balamurali S (2015) Performance analysis of classifier models to predict diabetes mellitus. Proc Comput Sci 47:45–51. https:// doi. org/ 10. 1016/j. procs. 2015. 03. 182 28. Tamilvanan B, Bhaskaran VM (2017) An experimental study of diabetes disease prediction system using classification techniques. IOSR J Comput Eng 19:39–44. https:// doi. org/ 10. 9790/0661- 19010 43944 29. Wang C, Li L, Wang L et al (2013) Evaluating the risk of type 2 diabetes mellitus using artificial neural network: An effective classification approach. Diabetes Res Clin Pract 100:111– 118.https:// doi. org/ 10. 1016/j. diabr es. 2013. 01. 023 30. Mounika M, Suganya SD, Vijayashanthi B, Anand SK (2015) Predictive analysis of diabetic treatment using classification algorithm. Int J Comput Sci Inf Technol 6:2502–2502 31. Naiarun N, Moungmai R (2015) Comparison of classifiers for the risk of diabetes prediction. Proc Comput Sci 69:132–142. https://doi. org/ 10. 1016/j. procs. 2015. 10. 014 32. Karthikeyani V, Begum I, Tajudin K, Begam I (2012) Comparative of data mining classification algorithm (CDMCA) in diabetes disease prediction. Int J Comput Appl 60:26–31. https:// doi. org/10. 5120/ 9745- 4307 33. Songthung P, Sripanidkulchai K (2016) Improving type 2 diabetes mellitus risk prediction using classification. In: International joint conference on computer science and software engineering (JCSSE), pp 1–6 34. Heydari M, Teimouri M, Heshmati Z, Alavinia SM (2016) Comparison of various classification algorithms in the diagnosis of type 2 diabetes in Iran. Int J Diabetes Dev Ctries 36:167–173. https:// doi. org/ 10. 1007/ s13410- 015- 0374-4 35. Kumar PS, Umatejaswi V (2017) Diagnosing diabetes using data mining techniques. Int J Sci Res Publ 7:705–709 36. Nithyapriya T, Dhinakaran S (2017) Analysis of various data mining classification techniques to predict diabetes mellitus. Int J Eng Dev Res 5:695–703 37. Sisodia D, Sisodia DS (2018) Prediction of diabetes using classification algorithms. Proc Comput Sci 132:1578–1585. https://doi. org/ 10. 1016/j. procs. 2018. 05. 122 38. Selvakumar S, Kannan KS, GothaiNachiyar S (2017) Prediction of diabetes diagnosis using classification based data mining techniques. Int J Stat Syst 12:183–188 39. Lai H, Huang H, Keshavjee K et al (2019) Predictive models for diabetes mellitus using machine learning techniques. BMC Endocr Disord 1:1–9. https:// doi. org/ 10. 1186/ s12902- 019- 0436-6 40. Perveen S, Shahbaz M, Gurgachi A, Keshavjee K (2016) Performance analysis of data mining classification techniques to predict diabetes. Proc Comput Sci 82:115–121. https:// doi. org/ 10. 1016/j.procs. 2016. 04. 016 41. Peter S (2014) An analytical study on early diagnosis and classification of diabetes mellitus. Bonfring Int J Data Min 4:07–11.https:// doi. org/ 10. 9756/ BIJDM. 10310 42. Komi M, Li J, Zhai Y, Zhang X (2017) Application of data mining methods in diabetes prediction. In: International conference on image, vision and computing (ICIVC), pp 1006– 1010 43. Karegowda AG, Jayaram M, Manjunath A (2012) Rule based classification for diabetic patients using cascaded K-means and decision tree C4.5. Int J Comput Appl. https:// doi. org/ 10. 5120/6836- 9460 44. Zou Q, Qu K, Luo Y et al (2018) Predicting diabetes mellitus with machine learning techniques. Front Genet. https:// doi. org/10. 3389/ fgene. 2018. 00515 45. Alehegn M, Joshi RR, Mulay P (2019) Diabetes analysis and prediction using random forest KNN Naïve Bayes and J48: an ensemble approach. Int J Sci Technol Res 8:1346–1354 46. NirmalaDevi M, alias Balamurugan SA, Swathi UV (2013) An amalgam KNN to predict diabetes mellitus. In: IEEE international conference on emerging trends in computing, communication and nanotechnology (ICECCN) 47. Bashir S, Qamar U, Khan FH, Javed MY (2014) An efficient rule based classification of diabetes using ID3, C4.5 & CART ensembles. In: 12th international conference on frontiers of information technology, pp 226–231 48. Kaur G, Chhabra A (2014) Improved J48 classification algorithm for the prediction of diabetes. Int J Comput Appl 98:13–17. https:// doi. org/ 10. 5120/ 17314- 7433 49. Ahmed K, Jesmin T (2014) Comparative analysis of data mining classification algorithms in type-2 diabetes prediction data using WEKA approach. Int J Sci Eng 7:155–160. https:// doi. org/ 10.12777/ ijse.7. 2. 155- 160 50. Srikanth P, Deverapalli D (2016) A critical study of classification algorithms using diabetes diagnosis. In: 2016 IEEE 6th international conference on advanced computing (IACC), pp 245– 249 51. Devi MR, Shyla JM (2016) Analysis of various data mining techniques to predict diabetes mellitus. Int J Appl Eng Res 11:727–730 52. 46. EMC Education Services (2015) Data science and big data analytics: discovering, analyzing, visualizing and presenting data. Wiley, New York 53. Oliver JJ, Hand D (1994) Averaging over decision stumps. In: European conference on machine learning, pp 231–241 54. Muralidharan V, Sugumaran V (2012) A comparative study of Naïve Bayes classifier and Bayes net classifier for fault diagnosis of monoblock centrifugal pump using wavelet analysis. Appl Soft Comput 12:2023–2029. https:// doi. org/ 10. 1016/j. asoc. 2012. 03.021 55. Aha DW, Kibler D, Albert MK (1991) Instance-based learning algorithms. Mach Learn 6:37– 66. https:// doi. org/ 10. 1007/ BF00153759 56. Cleary JG, Trigg LE (1995) K*: An instance-based learner using an entropic distance measure. Mach Learn Proc 1995:108–114 57. Homser Jr DW, Lemeshow S, Sturdivant RX (2013) Applied logistic regression 58. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273–297. https:// doi. org/ 10. 1007/ BF009 94018 59. Hassoun MH (1995) Fundamentals of artificial neural networks. MIT Press 60. Hall M, Frank E, Holmes G et al (2009) The WEKA data mining software: an update. ACM SIGKDD Explor Newsl 11:10–18.https:// doi. org/ 10. 1145/ 16562 74. 16562 78 61. Holte RC (1993) Very simple classification rules perform well on most commonly used datasets. Mach Learn 11:63–90. https:// doi.org/ 10. 1023/A: 10226 31118 932 62. Cohen WW (1995) Fast effective rule induction. In: Machine learning proceedings. Elsevier, pp 115–123 63. Kohavi R (1995) The power of decision tables. In: European conference on machine learning, pp 174–189 64. Pfahringer B (2010) Random model trees: an effective and scalable regression method 65. Liaw A, Wiener M (2002) Classification and regression by random forest. R news 2:18–22 66. Quinlan JR (1987) Simplifying decision trees. Int J Man Mach Stud 27:221–234. https:// doi. org/ 10. 1016/ S0020- 7373(87)80053-6 67. Alsabti K, Ranka S, Singh V (1997) An efficient K-means clustering algorithm 68. Breiman L (1996) Bagging predictors. Mach Learn 24:123–140. https:// doi. org/ 10. 69. Freund Y, Schapire RE (1996) Experiments with a new boosting algorithm. In: Thirteenth international conference on machine learning, pp 148–156 70. Wolpert DH (1992) Stacked generalization. Neural Netw 5:241– 259. https:// doi. org/ 10. 1016/ S0893- 6080(05) 80023-1 71. Leila Ismail, Huned Materwala1, Maryam Tayefi, Phuong Ngo, Achim P. Karduck Type 2 Diabetes with Artificial Intelligence Machine Learning: Methods and Evaluation 72. Archives of Computational Methods in Engineering (2022) 29:313–333 https://doi.org/10.1007/s11831-021-09582-x 73. Dehghan A, Van Hoek M, Sijbrands EJG et al (2008) High serum uric acid as a novel risk factor for type 2 diabetes. Diabetes Care 31:361–362. https:// doi. org/ 10. 2337/ dc07- 1276 74. Hypertension and Obesity. https://www.Obesity action.org/community/ article-library/ hypertension-and-obesity-how-weight-lossaffects- hypertension/. Accessed 23 Mar 2021 75. Cardiovascular (Heart) Diseases. https:// www. webmd. com/ heartdisease/ guide/ disea sescardi ovasc ular#1. Accessed 23 Mar 2021 76. Hall M, Frank E, Holmes G et al (2009) The WEKA data mining software: an update. ACM SIGKDD Explor Newsl 11:10–18. https:// doi. org/ 10. 1145/ 16562 74. 16562 78 77. Smith JW, Everhart J, Dickson W, et al (1988) Using the ADAP learning algorithm to forecast the onset of diabetes mellitus. In: Proceedings of the annual symposium on computer application in medical care, pp 261–265 78. Strack B, Deshazo JP, Gennings C et al (2014) Impact of HbA1c measurement on hospital readmission rates: analysis of 70,000 clinical database patient records. Biomed Res Int 2014:11. https://doi. org/ 10. 1155/ 2014/ 781670 79. Johnson AEW, Pollard TJ, Shen L et al (2016) MIMIC-III, a freely accessible critical care database. Sci Data. https:// doi. org/ 10. 1038/sdata. 2016. 35 80. Hall MA (1998) Correlation-based feature subset selection for machine learning 81. Hall MA (1999) Feature selection for discrete and numeric class machine learning 82. Feature Selection Algorithms. https:// dataminingntua. files. wordpress. com/ 2008/ 04/ wekaselect- attributes. pdf. Accessed 23 Mar 2021 83. Hou C, Carter B, Hewitt J, Francisa T, Mayor S. Do Mobile Phone Applications Improve Glycemic Control (HbA1c) in the Self-management of Diabetes? A systematic review, metaanalysis, and GRADE of 14 randomized trials. Diabetes Care 2016;39(11):2089–95. 84. Fagherazzi G, Ravaud P. Digital diabetes: perspectives for diabetes prevention, management and research. Diabetes Metab 2019;45(4):322–9. https://doi.org/10.1016/j.diabet.2018.08.012 85. Buch V, Varughese G, Maruthappu M. Artificial intelligence in diabetes care. Diabet Med 2018;35:495–7. 86. Akihiro Nomura, Masahiro Noguchi, Mitsuhiro Kometani, Kenji Furukawa, Takashi Yoneda Artificial Intelligence in Current Diabetes Management and Prediction Current Diabetes Reports (2021) 21: 61Vol.:(0112 33456789) https://doi.org/10.1007/s11892-021-01423-2