Evaluating the Risk of Type 2 Diabetes Mellitus Using Artificial Neural Network
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Abstract
To identify risk factors, neural network analysis is used to create disease prediction models, including diabetes. The goals of this study were to identify diabetes risk factors and determine their relative contribution using artificial intelligence as a mode of prediction. The current investigation was led by breaking down the dataset, as shown below. We chose a dataset from Kaggle. The diabetes dataset was from India. It has 763 female members, 497 of whom have no diabetes and 266 who have type 2 diabetes. We used neural network analysis to create mathematical models and visualize the distribution of diabetic risk factors. The significance level was set at 0.05. The current study found that the following risk factors were ranked in order of importance: Diabetes Pedigree Function, age, glucose, skin thickness, blood pressure, BMI, insulin, and number of pregnancies. When combined, neural network analysis is effective in developing mathematical models that can predict disease risk factors.
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References
I. Adeyemo AB, Akinwonmi AE. On the diagnosis of diabetes mellitus using artificial neural network model artificial neural network models. Afr J Comput Ict 2011; 4:1-8.
II. Tripolt NJ, Narath SH, Eder M, Pieber TR, Wascher TC, Sourij H. Multiple risk factor intervention reduces carotid atherosclerosis in patients with type 2 diabetes. Cardiovasc Diabetol 2014; 13:95.
III. Tuttolomondo A, Maida C, Maugeri R, Iacopino G, Pinto A. Relationship between diabetes and ischemic stroke: analysis of diabetes-related risk factors for stroke and of specific patterns of stroke associated with diabetes mellitus. J Diabetes Metab 2015; 6:544.
IV. World Health Organization. Prevention of blindness from diabetes mellitus: report of a WHO consultation in Geneva, Switzerland, 9-11 November 2005; 2006 [cited 2018 Mar 26]. Available from:
http://apps.who.int/iris/handle/10665/43576.
V. Nasri H, Rafiean-Kopaei M. Diabetes mellitus and renal failure: prevention and managment. J Res Med Sci 2015; 20:1112- 1120.
VI. World Health Organization. Global report on diabetes; 2016 [cited 2018 Mar 26]. Availablefrom:http://apps.who.int/iris/bitstream/10665/204871/1/9789241565257_eng.pdf
VII. Rawal LB, Tapp RJ, Williams ED, Chan C, Yasin S, Oldenburg B. Prevention of type
VIII. 2 diabetes and its complications in developing countries: a review. Int J Behav Med 2012; 19:121-133.
IX. Olaniyi EO, Adnan K. Onset diabetes diagnosis using artificial neural network. Int J Sci Eng Res 2014; 5:754-759.
X. Soltanian AR, Borzouei S, Afkhami- Ardekan M. Design, developing and validation a questionnaire to assess general population awareness about type II diabetes disease and its complications. Diabetes Metab Syndr 2017;11 Suppl 1: S39-S43.
XI. Adhikary M, Chellaiyan VG, Chowdhury R, Daral S, Taneja N, Kumar Das T. Association of risk factors of type 2 diabetes mellitus and fasting blood glucose levels among residents of rural area of Delhi: a cross sectional study. Int J Community Med Public Health 2017; 4:1005-1010.
XII. Binh TQ, Nhung BT. Prevalence, and risk factors of type 2 diabetes in middle-aged women in Northern Vietnam. Int J Diabetes Dev Ctries 2016; 36:150-157.
XIII. Lee YH, Shin MH, Nam HS, Park KS, Choi SW, Ryu SY, et al. Effect of family history of diabetes on hemoglobin A1c levels among individuals with and without diabetes: the dong-gu study. Yonsei Med J 2018; 59:92- 100.
XIV. van Zon SK, Snieder H, Bültmann U, Reijneveld SA. The interaction of socioeconomic position and type 2 diabetes mellitus family history: a cross-sectional analysis of the Lifelines Cohort and Biobank Study. BMJ Open 2017;7:e 015275.
XV. Zhang N, Yang X, Zhu X, Zhao B, Huang T, Ji Q. Type 2 diabetes mellitus unawareness, prevalence, trends and risk factors: National Health and Nutrition Examination Survey (NHANES) 19992010. J Int Med Res 2017; 45:594-609.
XVI. Suhail Khan M, Kumar Singh A, Bihari Gupta S, Saxena S, Maheshwari S. Assessment of risk factors of type 2 diabetes mellitus in an urban population of district bareilly. Indian J Forensic Community Med 2016; 3:5-9.
XVII. Mi SQ, Yin P, Hu N, Li JH, Chen XR, Chen B, et al. BMI, WC, WHtR, VFI and BFI: which indictor is the most efficient screening index on type 2 diabetes in Chinese community population. Biomed Environ Sci 2013; 26:485-491.
XVIII. Hackett RA, Steptoe A. Type 2 diabetes mellitus and psychological stress: a modifiable risk factor. Nat Rev Endocrinol 2017;13: 547-560.
XIX. Pan KY, Xu W, Mangialasche F, Fratiglioni L, Wang HX. Workrelated psychosocial stress and the risk of type 2 diabetes in later life. J Intern Med 2017; 281:601-610.
XX. Bertoglia MP, Gormaz JG, Libuy M, Sanhueza D, Gajardo A, Srur A, et al. The population impact of obesity, sedentary lifestyle, and tobacco and alcohol consumption on the prevalence of type2 diabetes: analysis of a health population survey in Chile, 2010. PLoS One 2017;12: e0178092.
XXI. Gao Y, Xie X, Wang SX, Li H, Tang HZ, Zhang J, et al. Effects of sedentary occupations on type 2 diabetes and hypertension in different ethnic groups in Northwest China. Diab Vasc Dis Res 2017; 14:372-375.
XXII. Maddatu J, Anderson-Baucum E, Evans-Molina C. Smoking and the risk of type 2 diabetes. Transl Res 2017; 184:101- 107.
XXIII. Beidokhti MN, Jäger AK. Review of antidiabetic fruits, vegetables, beverages, oils and spices commonly consumed in the diet. J Ethnopharmacol 2017; 201:26-41.
XXIV. Joseph JJ, Echouffo-Tcheugui JB, Golden SH, Chen H, Jenny NS, Carnethon MR, et al. Physical activity, sedentary behaviors and the incidence of type 2 diabetes mellitus: the Multi-Ethnic Study of Atherosclerosis (MESA). BMJ Open Diabetes Res Care 2016;4: e000185.
XXV. Smith AD, Crippa A, Woodcock J, Brage S. Physical activity and incident type 2 diabetes mellitus: a systematic review and dose response meta-analysis of prospective cohort studies. Diabetologia 2016; 59:2527- 2545
https://machinelearningmastery.com/case- study-predicting-the-onset-of-diabetes-within- five-years-part-1-of-3, retrieved in 17-9-2021.
XXVI. Rabindra Nath Das. Determinants of Diabetes Mellitus in the Pima Indian Mothers and Indian Medical Students. The Open Diabetes Journal, 2014, 7, 5-13.
XXVII. Knowler WC, Nelson RG, Saad MF, Bennett PH, Pettitt DJ. Determinants of diabetes mellitus in the Pima Indians. Diabetes Care, 1993; 16: 216-27.
XXVIII. Dort A, Ballintine EJ, Bennett PH, Miller M. Retinopathy in Pima Indians relationships to glucose level duration of diabetes age at diagnosis of diabetes and age at examination a population with a higher prevalence of diabetes mellitus. Diabetes 1976; 25: 554-60.
XXIX. Pettitt DJ, Knowler WC, Lisse JR, Bennett PH. Development of retinopathy and proteinuria in relation to plasma- glucose concentrations in Pima Indians. Lancet 1980; 4: 1050-2.
XXX. Collier A., et al. Change in skin thickness associated with cheiroarthropathy in insulin- dependent diabetes mellitus. British Medical 292 (1986): 936.
XXXI. Brownlee M., et al. non-enzymatic glycosylation and the pathogenesis of diabetic complications. Annals of Internal Medicine 101 (1984): 527-537.
XXXII. Kennedy L and Baynes JW. Non- enzymatic glycosylation and the chronic complications of diabetes: an overview. Diabetologia 26 (1984): 93-98.
XXXIII. Jain SM., et al. Evaluation of skin and subcutaneous tissue thickness at insulin injection sites in Indian, insulin naïve, type-2 diabetic adult population. Indian Journal of Endocrinology and Metabolism 17 (2013): 864-870.
XXXIV. Ahed J Alkhatib., et al. “Skin Thickness can Predict the Progress of Diabetes Type 2: A New Medical Hypothesis”. EC Diabetes and Metabolic Research 4.8 (2020): 08-12.
XXXV. Lee Y, Nelder JA, Pawitan Y. Generalized Linear Models with Random Effects (Unified Analysis via H-likelihood). London: Chapman & Hall 2006.
XXXVI. Sun D, Zhou T, Heianza Y, Li X, Fan M, Fonseca VA, Qi L. Type 2 Diabetes and Hypertension. Circ Res. 2019 Mar 15;124(6):930-937.
doi: 10.1161/CIRCRESAHA.118.314487. PMID: 30646822; PMCID: PMC6417940.
XXXVII. Polemiti, E., Baudry, J., Kuxhaus, O. et al. BMI and BMI change following incident type 2 diabetes and risk of microvascular and macrovascular complications: the EPIC- Potsdam study. Diabetologia 64, 814–825 (2021).
https://doi.org/10.1007/s00125-020-05362-7.
XXXVIII. Alkhatib AJ. Insulin as a predictor of diabetes type 2: a new medical hypothesis. Adv Obes Weight Manag Control. 2021;11(1):1-3.
DOI: 10.15406/aowmc.2021.11.00328.
XXXIX. Ahed J Alkhatib, New Insights of Diabetes: is it Rational to Initiate Insulin Treatment for Diabetes Type 2 Patients J Diabetes and Islet Biology 2(1) Doi: 10.31579/ 2641-8975/013.
XL. Mdoe MB, Kibusi SM, Munyogwa MJ, et al. Prevalence and predictors of gestational diabetes mellitus among pregnant women attending antenatal clinic in Dodoma region, Tanzania: an analytical cross- sectional study. BMJ Nutrition, Prevention & Health 2021;0. doi:10.1136/ bmjnph-2020-000149