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Neuropathic complications: Type II diabetes mellitus and other risky parameters using machine learning algorithms

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Abstract

The World Health Organization (WHO) reported in 2016 that close to 422 million adults live with diabetes. Diabetes is described as the United Kingdom's fastest-growing disease, which in recent decades has almost doubled in prevalence (Zghebi et al., Diabetes Obes Metab 19(11):1537–1545, https://doi.org/10.1111/dom.12964, 2019). The risk of DM is strongly advised, and the diabetes community should concentrate on identifying a pattern that leads to DM and its complications to avoid further complications. This study uses, logistic regression on PIMA Indian Diabetes Dataset, to identify the parameters and combination of parameters that lead to diabetic peripheral neuropathy (DPN) complications. Five different ML algorithms linear discriminant analysis (LDA), Naïve Bayes (NB), C5.0, SVM_RBF (Support Vector Machine_Radial Basis Function and gradient boosting machine (GBM) has been applied for the diagnosis of such disease for enhance the identified models using LR. The prediction performances are evaluated with four different performance metrics that is viz., accuracy, kappa, AUC-ROC, MCC measures. The critical combinations include (1) DM + Glucose + BP + BMI + Age, (2) DM + Glucose + BMI + Age, (3) DM + Glucose + BP + BMI + Age, (4) DM + Glucose + BP + BMI, (5) DM + Glucose + BMI. For each method in this study, LR is used in DPN prediction as the Accuracy and AUC are more close to reality. The results after calculations are: 0.79, 0.77, 0.787, 0.772 and 0.769 respectively, the AUC are 0.83 for all models and the MCC are around 0.5. The prediction was tested with other machine learning methods such as LDA, NB, C5.0, SVM_RBF and GBM have been close to 0.77 and the Kappa is 0.4. In the investigation, the patients should be tracked, and doctors should be notified to prescribe medications to ensure them from further complications, if the behavior indicates either of these combinations.

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Abbreviations

DM:

Diabetes mellitus

T2DM:

Type II diabetes mellitus

DPN:

Diabetic peripheral neuropathy

ML:

Machine learning

MLA:

Machine learning algorithm

LR:

Logistic regression

LDA:

Linear discriminant analysis

LDF:

Linear discriminant function

QDA:

Quadratic discriminant analysis

NB:

Naïve Bayes

SVM_RBF:

Support Vector Machine_Radial Basis Function

GBM:

Gradient boosting machine

AUC_ROC:

Area under the receiver operating curve

MCC:

Mathew’s correlation coefficient

BP:

Blood pressure

BMI:

Body Mass Index

KDD:

Knowledge discovery in data bases

RMSE:

Root mean square error

TP:

True positive

FP:

False positive

TN:

True negative

FN:

False negative

UCI:

University of California Irvine

VIF:

Variance inflation factor

MLE:

Maximum likelihood estimation

ANOVA:

Analysis of variance

PCA:

Principal component analysis

ANN:

Artificial neural network

DT:

Decision tree

IBM-SPSS:

International Business Machines Corporation-Statistical Package for the Social Sciences

HGS:

Handgrip strength

MNSI_Q; MNSI-PE:

Michigan Neuropathy Screening Instrument Questionnaire (MNSI-Q) and Physical Examination (MNSI-PE)

CNN; LSTM:

Convolutional long short term memory

RR:

R peak to R peak

ECG:

Electrocardiogram

References

  • Abdar M, Kalhori SR, Sutikno T, Subroto IM, Arji G (2015) Comparing performance of data mining algorithms in prediction heart diseases. Int J Electr Comput Eng. https://doi.org/10.11591/ijece.v5i6.pp1569-1576

    Article  Google Scholar 

  • Acharya UR et al (2016) Automated diagnosis of diabetes using entropies and diabetic index. J Mech Med Biol 16(01):1640008

    Article  Google Scholar 

  • Adam M, Ng EYK, Oh SL, Heng ML, Hagiwara Y, Tan JH, Tong JWK, Acharya UR (2018) Automated characterization of diabetic foot using nonlinear features extracted from thermograms. Infrared Phys Technol 89:325–337

    Article  Google Scholar 

  • Akter T, Akhter QS, Polly ZA, Debsarma S (2020) Risk factors for peripheral neuropathy in patients with diabetes mellitus. IMC J Med Sci 13(2):40–44. https://doi.org/10.3329/imcjms.v13i2.45285

    Article  Google Scholar 

  • Amin N, Doupis J (2016) Diabetic foot disease: from the evaluation of the ‘foot at risk’ to the novel diabetic ulcer treatment modalities. World J Diabetes 7(7):153. https://doi.org/10.4239/wjd.v7.i7.153

    Article  Google Scholar 

  • Atul DV (2016) Validity of P-peak to R-peak interval compared to classical PR-interval to assess dynamic beat-to-beat AV conduction variability on surface electrocardiogram. Certain distance degree based Topol. indices Zeolite LTAFram., December 2016, pp 11–14

  • Balcıoğlu AS (2015) Diabetes and cardiac autonomic neuropathy: Clinical manifestations, cardiovascular consequences, diagnosis and treatment. World J Diabetes 6(1):80. https://doi.org/10.4239/wjd.v6.i1.80

    Article  Google Scholar 

  • Battineni G, Chintalapudi N, Amenta F (2019) Machine learning in medicine: performance calculation of dementia prediction by support vector machines (SVM). Inform Med Unlocked 16:100200

    Article  Google Scholar 

  • Bunner AE, Wells CL, Gonzales J, Agarwal U, Bayat E, Barnard ND (2015) A dietary intervention for chronic diabetic neuropathy pain: a randomized controlled pilot study. Nutr Diabetes 5:e158. https://doi.org/10.1038/nutd.2015.8.www.nature.com/nutd

    Article  Google Scholar 

  • Chicco D, Jurman G (2020) The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genom 21(1):1–13. https://doi.org/10.1186/s12864-019-6413-7

    Article  Google Scholar 

  • Cho G, Yim J, Choi Y, Ko J, Lee SH (2019) Review of machine learning algorithms for diagnosing mental illness. Psychiatry Investig 16(4):262–269. https://doi.org/10.30773/pi.2018.12.21.2. www.psychiatryinvestigation.org(ISSN 1976–3026)

  • Cruz-Vega I, Hernandez-Contreras D, Peregrina-Barreto H, Rangel-Magdaleno JJ, Ramirez-Cortes JM (2020) Deep learning classification for diabetic foot thermograms. Sensors (Switzerland) 20(6):1–22. https://doi.org/10.3390/s20061762

    Article  Google Scholar 

  • Cui X et al (2020) On the variability of heart rate variability—evidence from prospective study of healthy young college students. Entropy 22(11):1–26. https://doi.org/10.3390/e22111302

    Article  Google Scholar 

  • Dagliati A, Marini S, Sacchi L, Cogni G, Teliti M, Tibollo V, De Cata P, Chiovato L, Bellazzi R (2018) Machine learning methods to predict diabetes complications. J Diabetes Sci Technol 12(2):295–302

    Article  Google Scholar 

  • de Vlaming R, Groenen PJF (2015) The current and future use of ridge regression for prediction in quantitative genetics. Biomed Res Int 2015:18. https://doi.org/10.1155/2015/143712(ArticleID143712)

    Article  Google Scholar 

  • Dhahri H, Al Maghayreh E, Mahmood A, Elkilani W, Faisal Nagi M (2019) Automated breast cancer diagnosis based on machine learning algorithms. J Healthc Eng 2019:11. https://doi.org/10.1155/2019/4253641(Article ID 4253641)

  • Duzan H, Shariff NSBM (2015a) Ridge regression for solving the multicollinearity problem: review of methods and models. J Appl Sci 15(3):392–404. https://doi.org/10.3923/jas.2015.392.404

    Article  Google Scholar 

  • Duzan H, Shariff NSBM (2015) Ridge regression for solving the multicollinearity problem: review of methods and models. J Appl Sci 15(3):392–404 (ISSN 1812-5654)

  • Eleftheriadou I, Dimitrakopoulou N, Kafasi N, Tentolouris A, Dimitrakopoulou A, Anastasiou IA, Entolouris N (2019) Endothelial progenitor cells and peripheral neuropathy in subjects with type 2 diabetes mellitus. J Diabetes Complicat. https://doi.org/10.1016/j.jdiacomp.2019.107517

    Article  Google Scholar 

  • Holobar A, Farina D (2014) Blind source identification from the multichannel surface electromyogram. Physiol Meas 35(7):R143–R165. https://doi.org/10.1088/0967-3334/35/7/r143

    Article  Google Scholar 

  • Jaiswal M, Divers J, Dabelea D, Isom S, Bell RA (2018) Prevalence of and risk factors for diabetic peripheral neuropathy in youth with Type 1 and Type 2 Diabetes: SEARCH for diabetes in youth study. Diabetes Care 40:1226–1232

    Article  Google Scholar 

  • James WLJ, Mayberry R, Yoo W (2014) A study of effects of multicollinearity in the multivariable analysis. Int J Appl Sci Technol 4(5):9–19

    Google Scholar 

  • Jelinek HF, Cornforth DJ, Kelarev AV (2016) Machine learning methods for automated detection of severe diabetic neuropathy. J Diabetic Complications Med 1:108. https://doi.org/10.4172/jdcm.1000108

    Article  Google Scholar 

  • Kaur H, Kumari V (2018) Predictive modelling and analytics for diabetes using a machine learning approach. Appl Comput Inf. https://doi.org/10.1016/j.aci.2018.12.004

    Article  Google Scholar 

  • Kaur H, Kumari V (2019) Predictive modelling and analytics for diabetes using a machine learning approach. Appl Comput Inform. https://doi.org/10.1016/j.aci.2018.12.004

    Article  Google Scholar 

  • Kavakiotis I, Tsave O, Salifoglou A, Maglaveras N, Vlahavas I, Chouvarda I (2017) Machine learning and data mining methods in diabetes research. Comput Struct Biotechnol J 15:104–116

    Article  Google Scholar 

  • Kazemi M, Moghimbeigi A, Kiani J, Mahjub H, Faradmal J (2016) Diabetic peripheral neuropathy class prediction by multicategory support vector machine model: a cross-sectional study. Epidemiol Health 38:e2016011. https://doi.org/10.4178/epih/e2016011(eCollection2016)

    Article  Google Scholar 

  • Khawaja NM, Abu-Shennar J (2018) The prevalence and risk factors of peripheral neuropathy among patients with type 2 diabetes mellitus; the case of Jordan. Diabetol Metab Syndr. https://doi.org/10.1186/s13098-018-0309-6

    Article  Google Scholar 

  • Kheradmand F, Khamseh AA, Khoraskani MM (2017) Application and comparison of neural network, C5.0, and classification and regression trees (CART) algorithms in the credit risk evaluation problem (case study: a standard German credit dataset). Int J Knowl Eng Data Min 4(1):1. https://doi.org/10.1504/ijkedm.2017.10006181

  • Kim ES, Lee SW, Mo EY, Moon SD, Han JH (2015) Inverse association between serum total billirubin levels and diabetic peripheral neuropathy in patients with type 2 diabetes. Endocrine 50(2):405–412

    Article  Google Scholar 

  • Koo YS, Jung KY, Lee SH, Cho CS, Yang KS, Jang JH, Kim BJ (2013) Multichannel surface electrodes increase the sensitivity of diagnosis of neuropathy in diabetic patients. J Electromyogr Kinesiol 23:1057–1064

    Article  Google Scholar 

  • Kumar A, Ranjan A, Chand G, Kumar D, Singh SK, Kumar V (2015) The foot care process of diabetic patients (with and without foot ulcer) attending a Tertiary Care Hospital in India. J Stem Cell Res Therapy. https://doi.org/10.4172/2157-7633.1000280

    Article  Google Scholar 

  • Lagani V, Chiarugi F, Thomson S, Fursse J, Lakasing E, Jones RW, Tsamardinos I (2015) Development and validation of risk assessment models for diabetes-related complications based on the DCCT/EDIC data. J Diabetes Complicat 29:479–487

    Article  Google Scholar 

  • Lai H, Huang H, Keshavjee K, Guergachi A, Gao X (2019) Predictive models for diabetes mellitus using machine learning techniques. BMC Endocr Disord 19(1):1–9. https://doi.org/10.1186/s12902-019-0436-6

    Article  Google Scholar 

  • Le Cessie S, Houwelingen JCV (1992) Ridge estimators in logistic regression. J R Stat Soc Ser C (Appl Stat) 41:191–201

    MATH  Google Scholar 

  • Li B-Q, Zheng L-L, Feng K-Y, Hu L-L, Huang G-H, Chen L (2015) Prediction of linear B-cell epitopes with mRMR feature selection and analysis. Curr Bioinform 11(1):22–31

    Article  Google Scholar 

  • Lin CL, Fan CL (2019) Evaluation of CART, CHAID, and QUEST algorithms: a case study of construction defects in Taiwan. J Asian Archit Build Eng 18(6):539–553. https://doi.org/10.1080/13467581.2019.1696203

    Article  Google Scholar 

  • Liu S, Gao Y, Shen Y, Zhang M, Li J, Sun P (2019) Application of three statistical models for predicting the risk of diabetes. BMC Endocr Disord 19(1):1–10. https://doi.org/10.1186/s12902-019-0456-2

    Article  Google Scholar 

  • Malanda UL, Bot SD, Nijpels G (2013) Self-monitoring of blood glucose in noninsulin-using type 2 diabetic patients: It is time to face the evidence. Diabetes Care 36(1):176–178. https://doi.org/10.2337/dc12-0831

    Article  Google Scholar 

  • Maniruzzaman M, Rahman MJ, Al-MehediHasan M, Suri HS, Abedin MM, El-Baz A, Suri JS (2018) Accurate diabetes risk stratification using machine learning: role of missing value and outliers. J Med Syst 42:92. https://doi.org/10.1007/s10916-018-0940-7

    Article  Google Scholar 

  • Mao F, Zhu X, Liu S, Qiao X, Zheng H, Lu B (2019) Age as an independent risk factor for diabetic peripheral neuropathy in Chinese patients with Type 2 diabetes. Aging Dis 10(3):592–600

    Article  Google Scholar 

  • Mayooran T (2018) A gradient-based optimization algorithm for ridge regression by using R. Int J Res Sci Innov 5(4):38–44 (ISSN 2321–2705)

  • Metz CE (2008) ROC analysis in medical imaging: a tutorial review of the literature. Radiol Phys Technol 1(1):2–12. https://doi.org/10.1007/s12194-007-0002-1

    Article  Google Scholar 

  • Mirzajani SS, Salimi S (2018) Prediction and diagnosis of diabetes by using data mining techniques. Avicenna J Med Biochem 6(1):3–7. https://doi.org/10.15171/ajmb.2018.02

    Article  Google Scholar 

  • Nascimento OJMD, Castelo C, Pupe B, Boiteux E, Cavalcanti U (2016) Diabetic neuropathy neuropatia diabética. Rev Dor São Paulo 17(Suppl 1):46–51. https://doi.org/10.5935/1806-0013.20160047

    Article  Google Scholar 

  • Nirmaladevi K, Shanthi S, Agila T, Dharani RT, Dhivyapriya P (2020) Analysis and prediction of diabetes using machine learning. Test Eng Manag 83(9):14533–14538

    Google Scholar 

  • Nisar MU et al (2015) Association of diabetic neuropathy with duration of Type 2 diabetes and glycemic control. Cureus. https://doi.org/10.7759/cureus.302

    Article  Google Scholar 

  • Oh TJ, Kang S, Lee J-E, Moon JH, Choi SH, Lim S, Jang HC (2019) Association between deterioration in muscle strength and peripheral neuropathy in people with diabetes. J Diabetes Complicat. https://doi.org/10.1016/j.jdiacomp.2019.04.00

    Article  Google Scholar 

  • Padma S, Murugan R, Senthilkumaran M (2018) Risk factors deciding the prevention and healing of diabetic foot ulcer: a prospective study in Chennai Medical College Hospital and Research Center Irungalur, Trichy, Tamil Nadu, India; a rural based tertiary medical care centre. Int Surg J 5(5):1770–1775. http://www.ijsurgery.com

  • Pop-Busui R et al (2017) Diabetic neuropathy: a position statement by the American diabetes association. Diabetes Care 40(1):136–154. https://doi.org/10.2337/dc16-2042

    Article  Google Scholar 

  • Río X, Larrinaga-Undabarrena A, Coca A, Guerra-Balic M (2020) Reference values for handgrip strength in the Basque country elderly population. Biology (Basel) 9(12):1–12. https://doi.org/10.3390/biology9120414

    Article  Google Scholar 

  • Salh SM (2014) Using ridge regression model to solving multicollinearity problem. Int J Sci Eng Res 5(10):992 (ISSN 2229-5518)

  • Senaviratna NA and Cooray TM (2019) Diagnosing multicollinearity of logistic regression model. Asian J Probab Stat 5(2):1–9 (ISSN: 2582-0230; Article no. AJPAS.51693)

  • Su JB, Zhao LH, Zhang XL, Cai HL, Huang HY, Xu F, Chen T, Wang XQ (2018) HbA1c variability and diabetic peripheral neuropathy in type 2 diabetic patients. Cardiovasc Diabetol 17:47. https://doi.org/10.1186/s12933-018-0693-0

    Article  Google Scholar 

  • Sudharsan B, Peeples M, Shomali M (2014) Hypoglycemia prediction using machine learning models for patients with Type 2 diabetes. J Diabetes Sci Technol 9(1):86–90

  • Sudharsan B, Peeples M, Shomali M (2015) Hypoglycemia prediction using machine learning models for patients with Type 2 diabetes. J Diabetes Sci Technol. https://doi.org/10.1177/1932296814554260

    Article  Google Scholar 

  • Suri HS, El-Baz AS, Suri JS (2017) “Comparative approaches for classification of diabetes mellitus data: machine learning paradigm. Comput Methods Prog Biomed 152:23–34

    Article  Google Scholar 

  • Swapna G, Vinayakumar R, Soman KP (2018) Diabetes detection using deep learning algorithms, The Korean Institute of Communications and Information Sciences (KICS). ICT Express 4:243–246

    Article  Google Scholar 

  • Tahrani AA, Altaf QA, Piya MK, Barnett AH (2017) Peripheral and autonomic neuropathy in South Asians and White Caucasians with Type 2 diabetes mellitus: possible explanations for epidemiological differences. J Diabetes Res. https://doi.org/10.1155/2017/1273789

    Article  Google Scholar 

  • Teich MC (2012) Heart rate variability: measures and models heart rate variability: measures and models Malvin C. Teich, Steven B. Lowen, Bradley M. Jost, and Karin Vibe-Rheymer Department of Electrical and Computer Engineering, Boston University, 8 Saint Mary’s Street, Boston, MA 02215 and Conor Heneghan Department of Electronic and Electrical Engineering, University College Dublin, Bel eld, Dublin 4, Ireland, no. November 2012, 2000

  • Van Eetvelde BLM, Lapauw B, Proot P, Vanden Wyngaert K, Celie B, Cambier D, Calders P (2020) The impact of sensory and/or sensorimotor neuropathy on lower limb muscle endurance, explosive and maximal muscle strength in patients with type 2 diabetes mellitus. J Diabetes Complicat. https://doi.org/10.1016/j.jdiacomp.2020.107562

    Article  Google Scholar 

  • Verma AK, Pal S, Kumar S (2019) Comparison of skin disease prediction by feature selection using ensemble data mining techniques. Inf Med Unlocked. https://doi.org/10.1016/j.imu.2019.100202

    Article  Google Scholar 

  • Wallert J, Tomasoni M, Madison G, Held C (2017) Predicting two-year survival versus nonsurvival after first myocardial infarction using machine learning and Swedish national register data. BMC Med Inform Decis Mak 17:99. https://doi.org/10.1186/s12911-017-0500-y

    Article  Google Scholar 

  • Williams BM et al (2020) An artificial intelligence-based deep learning algorithm for the diagnosis of diabetic neuropathy using corneal confocal microscopy: a development and validation study. Diabetologia 63(2):419–430. https://doi.org/10.1007/s00125-019-05023-4

    Article  Google Scholar 

  • Wu H, Yang S, He J (2018) Type 2 diabetes mellitus prediction model based on data mining. Inf Med Unlocked 10:100–107

    Article  Google Scholar 

  • Xu L, Lin X, Guan M, Liu Y (2018) Correlation between different stages of diabetic nephropathy and neuropathy in patients with T2DM: a cross-sectional controlled study. Diabetes Ther 9(6):2335–2346. https://doi.org/10.1007/s13300-018-0519-9

    Article  Google Scholar 

  • Xu Q, Wang L, Sansgiry SS (2020) A systematic literature review of predicting diabetic retinopathy, nephropathy and neuropathy in patients with type 1 diabetes using machine learning. J Med Artif Intell 3(MI):6. https://doi.org/10.21037/jmai.2019.10.04

    Article  Google Scholar 

  • Yang J, Yan PJ, Wan Q, Li H (2017) Association between hemoglobin levels and diabetic peripheral neuropathy in patients with type 2 diabetes: a cross-sectional study using electronic health records. J Diabetes Res. https://doi.org/10.1155/2017/2835981

    Article  Google Scholar 

  • Ybarra J, Jurado J, Garcia M, Zabaleta E, Mir-Coll T, Zabalegui A (2016) Cardiovascular disease predicts diabetic peripheral polyneuropathy in subjects with type 2 diabetes: a 10-year prospective study. Eur J Cardiovasc Nurs 15(4):248–325

    Article  Google Scholar 

  • Zghebi SS, Steinke DT, Carr MJ, Rutter MK, Emsley RA, Ashcroft DM (2017) Examining trends in type 2 diabetes incidence, prevalence and mortality in the UK between 2004 and 2014. Diabetes Obes Metab 19(11):1537–1545. https://doi.org/10.1111/dom.12964

    Article  Google Scholar 

  • Zhang YY, Zhou XB, Wang QZ, Zhu XY (2017) Quality of reporting of multivariable logistic regression models in Chinese clinical medical journals. Medicine 96(21):e6972, https://doi.org/10.1097/MD.0000000000006972. www.md-journal.com

  • Zhang Z, Zhao Y, Canes A, Steinberg D, Lyashevska O (2019) Predictive analytics with gradient boosting in clinical medicine. Ann Transl Med 7(7):152

    Article  Google Scholar 

  • Zhou YH, Gallins P (2019) A review and tutorial of machine learning methods for microbiome host trait prediction. Front Genet. https://doi.org/10.3389/fgene.2019.00579.www.frontiersin.org

    Article  Google Scholar 

  • Zou Q, Qu K, Luo Y, Yin D, Ju Y, Tang H (2018) Predicting diabetes mellitus with machine learning techniques. Front Genet 9:515. www.frontiersin.org

  • Zweig M, Campbell G (1993) Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin Chem 394:561–77

    Article  Google Scholar 

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  1. Department of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai, India

    R. Usharani

  2. Department of Information Technology, SRM Institute of Science and Technology, Chennai, India

    A. Shanthini

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Usharani, R., Shanthini, A. Neuropathic complications: Type II diabetes mellitus and other risky parameters using machine learning algorithms. J Ambient Intell Human Comput (2021). https://doi.org/10.1007/s12652-021-02972-w

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