ARTIFICIAL INTELLIGENCE AS THE BASIS OF DIGITAL DIABETES THERAPY (literature review)
Keywords:
diabetes mellitus, artificial intelligence, digital therapyAbstract
In the age of digitalization, it is vital to use modern methods of information technology. If earlier technologies were based on mathematical modeling, now artificial intelligence is widely used for decision making. The article presents data on "digital therapy" as a modern method of managing diseases, in this case diabetes.
References
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.
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.
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.
Papatheodorou K, Papanas N, Banach M, Papazoglou D, Edmonds M. Complications of diabetes 2016. J Diabetes Res 2016; 2016:6989453.
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.
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.
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.
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
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.
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
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.
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.
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
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.
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.
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.
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.
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.
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
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
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
Patel PB, Shah PP, Patel HD (2017) Analyze data mining algorithms for prediction of diabetes. Comput Eng 5:466–473
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
Hina S, Shaikh A, Sattar SA (2017) Analyzing diabetes datasets using data mining. J Basic Appl Sci 13:466–471
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
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
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
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
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
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
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
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
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
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
Kumar PS, Umatejaswi V (2017) Diagnosing diabetes using data mining techniques. Int J Sci Res Publ 7:705–709
Nithyapriya T, Dhinakaran S (2017) Analysis of various data mining classification techniques to predict diabetes mellitus. Int J Eng Dev Res 5:695–703
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
Selvakumar S, Kannan KS, GothaiNachiyar S (2017) Prediction of diabetes diagnosis using classification based data mining techniques. Int J Stat Syst 12:183–188
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
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
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
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
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
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
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
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)
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
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
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
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
Devi MR, Shyla JM (2016) Analysis of various data mining techniques to predict diabetes mellitus. Int J Appl Eng Res 11:727–730
46. EMC Education Services (2015) Data science and big data analytics: discovering, analyzing, visualizing and presenting data. Wiley, New York
Oliver JJ, Hand D (1994) Averaging over decision stumps. In: European conference on machine learning, pp 231–241
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
Aha DW, Kibler D, Albert MK (1991) Instance-based learning algorithms. Mach Learn 6:37–66. https:// doi. org/ 10. 1007/ BF00153759
Cleary JG, Trigg LE (1995) K*: An instance-based learner using an entropic distance measure. Mach Learn Proc 1995:108–114
Homser Jr DW, Lemeshow S, Sturdivant RX (2013) Applied logistic regression
Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273–297. https:// doi. org/ 10. 1007/ BF009 94018
Hassoun MH (1995) Fundamentals of artificial neural networks. MIT Press
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
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
Cohen WW (1995) Fast effective rule induction. In: Machine learning proceedings. Elsevier, pp 115–123
Kohavi R (1995) The power of decision tables. In: European conference on machine learning, pp 174–189
Pfahringer B (2010) Random model trees: an effective and scalable regression method
Liaw A, Wiener M (2002) Classification and regression by random forest. R news 2:18–22
Quinlan JR (1987) Simplifying decision trees. Int J Man Mach Stud 27:221–234. https:// doi. org/ 10. 1016/ S0020- 7373(87)80053-6
Alsabti K, Ranka S, Singh V (1997) An efficient K-means clustering algorithm
Breiman L (1996) Bagging predictors. Mach Learn 24:123–140. https:// doi. org/ 10. 1007/BF000 58655
Freund Y, Schapire RE (1996) Experiments with a new boosting algorithm. In: Thirteenth international conference on machine learning, pp 148–156
Wolpert DH (1992) Stacked generalization. Neural Netw 5:241– 259. https:// doi. org/ 10. 1016/ S0893- 6080(05) 80023-1
Leila Ismail, Huned Materwala1, Maryam Tayefi, Phuong Ngo, Achim P. Karduck Type 2 Diabetes with Artificial Intelligence Machine Learning: Methods and Evaluation
Archives of Computational Methods in Engineering (2022) 29:313–333 https://doi.org/10.1007/s11831-021-09582-x
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
Hypertension and Obesity. https://www.Obesity action.org/community/ article-library/ hypertension-and-obesity-how-weight-lossaffects- hypertension/. Accessed 23 Mar 2021
Cardiovascular (Heart) Diseases. https:// www. webmd. com/ heartdisease/ guide/ disea ses- cardi ovasc ular#1. Accessed 23 Mar 2021
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
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
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
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
Hall MA (1998) Correlation-based feature subset selection for machine learning
Hall MA (1999) Feature selection for discrete and numeric class machine learning
Feature Selection Algorithms. https:// dataminingntua. files. wordpress. com/ 2008/ 04/ weka- select- attributes. pdf. Accessed 23 Mar 2021
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, meta-analysis, and GRADE of 14 randomized trials. Diabetes Care 2016;39(11):2089–95.
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
Buch V, Varughese G, Maruthappu M. Artificial intelligence in diabetes care. Diabet Med 2018;35:495–7.
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