Abstract
Identification and quantification of insulin resistance require specific blood test which is complex, time-consuming, and much more invasive, making it difficult to track the changes daily. With the advancement in machine learning approaches, identification of insulin resistance can be carried out without clinical processes. In this work, insulin resistance is identified for individuals with triglycerides and HDL-c ratio using non-invasive techniques employing machine learning approaches. Eighteen parameters are used for identification purposes like age, sex, waist size, height, etc., and combinations of these parameters. Experiments are conducted over the CALERIE dataset. Each output of the attribute selection system is modeled over distinct calculations like logistic regression, CARTs, SVM, LDA, KNN, extra trees classifier. The proposed work is validated with a stratified cross-validation test. Results show that KNN and CatBoost show the best results with an accuracy of 74% and 73% respectively and 1% variance compared to 66% with Bernardini et al. and Stawiski et al. and 83% with Farran et al. With the proposed approach an individual can predict the insulin resistance and hence prospective chances of diabetes might be tracked daily using non-clinical approaches. While the same is not practically possible with clinical processes daily.
Similar content being viewed by others
Abbreviations
- HDL-c:
-
High-density lipoprotein-c
- CALERIE:
-
Comprehensive assessment of long-term effects of reducing intake of energy
- CART:
-
Classification and regression trees
- SVM:
-
Support vector machines
- LDA:
-
Linear discriminant analysis
- KNN:
-
K-nearest neighbour
References
Ormazabal V, Nair S, Elfeky O, Aguayo C, Salomon C, Zuñiga FA (2018) Association between insulin resistance and the development of cardiovascular disease. Cardiovasc Diabetol 17(122):1–14. https://doi.org/10.1186/s12933-018-0762-4
Saeedi P, Petersohn I, Salpea P, Bright D, Williams R (2019) Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: results from the International Diabetes Federation. Diabetes Atlas Ed Diabetes Res Clin Pract 157(107843):1–10. https://doi.org/10.1016/j.diabres.2019.107843
Freeman AM, Pennings N (2020 Jan) Insulin Resistance (Updated 2020 July 10). In: StatPearls (Internet). Treasure Island (FL): StatPearls Publishing. https://www.ncbi.nlm.nih.gov/books/NBK507839/. Accessed October 5, 2020
Woolcott OO, Bergman RN (2019) Relative Fat Mass as an estimator of whole-body fat percentage among children and adolescents. A cross-sectional study using NHANES. Sci Rep 9:15279. https://doi.org/10.1038/s41598-019-51701-z
Global Report on Diabetes, WHO, ISBN 978 92 4 156525 7, 2016. https://www.who.int/diabetes/global-report/en/. Accessed March 1, 2020
Ren X et al (2016) Association between triglyceride to HDL-C ratio (TG/HDL-C) and insulin resistance in chinese patients with newly diagnosed type 2 diabetes mellitus. PLoS ONE. https://doi.org/10.1371/journal.pone.0154345
Kamil ZIA, Jalaludin MY, Zin RMWM, Zain FM (2017) Triglyceride to HDL-C ratio is associated with insulin resistance in overweight and obese children. Sci Rep 7:40055. https://doi.org/10.1038/srep40055
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
Tafa Z, Pervetica N, Karahoda B (2015) An intelligent system for diabetes prediction. In: Proceedings of the 2015; 4th mediterranean conference on embedded computing (MECO), Budva, Montenegro, pp 378–382
Mercaldo F, Nardone V, Santone A (2017) Diabetes mellitus aected patients classification and diagnosis through machine learning techniques. Proc Comput Sci 112:2519–2528
Negi A, Jaiswal V (2016) A first attempt to develop a diabetes prediction method based on different global datasets. In: Proceedings of the 2016 4th international conference on parallel, distributed and grid computing (PDGC),Waknaghat, India, pp 237–241
Bernardini M, Morettini M, Romeo L, Frontoni E, Burattini L (2019) TyG-er: an ensemble Regression Forest approach for identification of clinical factors related to insulin resistance condition using Electronic Health Records. Comput Biol Med 112:103358
Yuvaraj N, SriPreethaa KR (2017) Diabetes prediction in healthcare systems using machine learning algorithms on Hadoop cluster. Clust Comput 22:1–9
Olaniyi EO, Adnan K (2014) Onset diabetes diagnosis using artificial neural network. Int J Sci Eng Res 5:754–759
Soltani Z, Jafarian A (2016) A new artificial neural networks approach for diagnosing diabetes disease type II. Int J Adv Comput Sci Appl 7:89–94
Sarwar A, Sharma V (2013) Comparative analysis of machine learning techniques in prognosis of type II diabetes. AI Soc 29(1):123–129. https://doi.org/10.1007/s00146-013-0456-0
Durairaj M, Kalaiselvi G (2015) Prediction of diabetes using back propagation algorithm. Int J Innov Technol 1:21–25
Maniruzzaman M, Kumar N, Menhazul Abedin M, Shaykhul Islam M, Suri HS, El-Baz AS, Suri JS (2017) Comparative approaches for classification of diabetes mellitus data: machine learning paradigm. Comput Methods Programs Biomed 152:23–34
Mirshahvalad R, Zanjani NA (2017) Diabetes prediction using ensemble perceptron algorithm. In: Proceedings of the 2017 9th international conference on computational intelligence and communication networks (CICN), Girne, Cyprus, pp 190–194
Sun X, Yu X, Liu J, Wang H (2017) Glucose prediction for type 1 diabetes using KLMS algorithm. In: Proceedings of the 2017 36th Chinese control conference (CCC), Liaoning, China, pp 1124–1128
Sisodia D, Sisodia DS (2018) Prediction of diabetes using classification algorithms. Proc Comput Sci 132:1578–1585
Ashiquzzaman A, Kawsar Tushar A, Rashedul Islam MD, Shon D, Kichang LM, Jeong-Ho P, Dong-Sun L, Jongmyon K (2018) Reduction of overfitting in diabetes prediction using deep learning neural network. In: Kim KJ, Kim H, Baek N (eds) IT convergence and security 2017, lecture notes in electrical engineering. Springer, Singapore, pp 449 35–43. https://doi.org/10.1007/978-981-10-6451-7_5
Swapna G, Soman KP, Vinayakumar R (2018) Automated detection of diabetes using CNN and CNN-LSTM network and heart rate signals. Proc Comput Sci 132:1253–1262
Mohebbi A, Aradóttir TB, Johansen AR, Bengtsson H, Fraccaro M, Mørup M (2017) A deep learning approach to adherence detection for type 2 diabetics. In: Proceedings of the 2017 39th annual international conference of the IEEE engineering in medicine and biology society (EMBC), Jeju, Korea, pp 2896–2899
Miotto R, Li L, Kidd BA, Dudley JT (2016) Deep patient: an unsupervised representation to predict the future of patients from the electronic health records. Sci Rep 6:26094
Pham T, Tran T, Phung D, Venkatesh S (2017) Predicting healthcare trajectories from medical records. A deep learning approach. J Biomed Inform 69:218–229
Askarzadeh A, Rezazadeh A (2013) Artificial neural network training using a new efficient optimization algorithm. Appl Soft Comput 13:1206–1213
Rao NM, Kannan K, Gao XZ, Roy DS (2018) Novel classifiers for intelligent disease diagnosis with multi-objective parameter evolution. Comput Electr Eng 67:483–496
Rahimloo P, Jafarian A (2016) Prediction of diabetes by using artificial neural network. logistic regression statistical model and combination of them. Bull Soc R Sci Liège 85:1148–1164
Gill NS, Mittal PA (2016) Computational hybrid model with two level classification using SVM and neural network for predicting the diabetes disease. J Theor Appl Inf Technol 87:1–10
NirmalaDevi M, Alias Balamurugan SA, Swathi UV (2013) An amalgam KNN to predict diabetes mellitus. In: Proceedings of the 2013 IEEE international conference ON emerging trends in computing, communication and nanotechnology (ICECCN), Tirunelveli, India, pp 691–695
Gylling H, Hallikainen M, Pihlajamäki J, Simonen P, Kuusisto J, Laakso M, Miettinen TA (2010) Insulin sensitivity regulates cholesterol metabolism to a greater extent than obesity. Lessons from the METSIM Study. JLR J Lipid Res 51:2422–2427
Krishnan E, Pandya BJ, Chung L, Hariri A, Dabbous O (2012) Hyperuricemia in young adults and risk of insulin resistance, prediabetes, and diabetes: a 15-year follow-up study. Am J Epidemiol 176:108–116
de Vries MA, Alipour A, Klop B, van de Geijn GJM, Janssen HW, Njo TL, van der Meulen N, Rietveld AP, Liem AH, Westerman EM, de Herder WW, Cabezas MC (2015) Glucose-dependent leukocyte activation in patients with type 2 diabetes mellitus, familial combined hyperlipidemia and healthy controls. Metabolism 64:213–217
Lee DJ, Choi JS, Kim KM, Joo NS, Lee SH, Kim KN (2014) Combined effect of serum gamma-glutamyltransferase and uric acid on Framingham risk score. Arch Med Res 45:337–342. https://doi.org/10.1016/j.arcmed.2014.04.004
Riaz S (2015) Study of protein biomarkers of diabetes mellitus type 2 and therapy with vitamin B1. J Diabetes Res 2015:150176. https://doi.org/10.1155/2015/150176
Stawiski K, Pietrzak I, Młynarski W, Fendler W, Szadkowska A (2018) NIRCa: an artificial neural network-based insulin resistance calculator. Pediatric Diabetes 19(2):231–235. https://doi.org/10.1111/pedi.12551
Choi BG, Rha SW, Kim SW, Kang JH, Park JY, Noh YK (2019) Machine learning for the prediction of new-onset diabetes mellitus during 5-year follow-up in non-diabetic patients with cardiovascular risks. Yonsei Med J 60(2):191–199. https://doi.org/10.3349/ymj.2019.60.2.191
Farran B, AlWotayan R, Alkandari H, Al-Abdulrazzaq D, Channanath A, Thanaraj TA (2019) Use of non-invasive parameters and machine-learning algorithms for predicting future risk of type 2 diabetes: a retrospective cohort study of health data from Kuwait. Front Endocrinol 10:624. https://doi.org/10.3389/fendo.2019.00624
Kraus WE et al (2019) 2 years of calorie restriction and cardiometabolic risk (CALERIE): exploratory outcomes of a multicentre, phase 2, randomised controlled trial. Lancet Diabetes Endocrinol 7:673–683
Jones AG, Hattersley AT (2013) The clinical utility of C-peptide measurement in the care of patients with diabetes. Diabetic Med 30:803–817. https://doi.org/10.1111/dme.12159
Pagana KD, Pagana TJ, Pagana TN (2019) Mosby’s diagnostic and laboratory test reference, 14th edn. Elsevier, St. Louis
Zheng X, Huang B, Luo S, Yang D, Bao W, Li J, Yao B, Weng J, Yan J (2017) A new model to estimate insulin resistance via clinical parameters in adults with type 1 diabetes. Diabetes Metab Res Rev. https://doi.org/10.1002/dmrr.2880
Liu Y (2020) Artificial intelligence-based neural network for the diagnosis of diabetes: model development. JMIR Med Inform 8(5):e18682. https://doi.org/10.2196/18682
Rodbard D (2017) Continuous glucose monitoring: a review of recent studies demonstrating improved glycemic outcomes. Diabetes Technol Therap 19(S3):S25–S37. https://doi.org/10.1089/dia.2017.0035
Ciudin A, Simó-Servat O, Hernández C, Arcos G, Diego S, Sanabria Á, Sotolongo Ó, Hernández I, Boada M, Simó R (2017) Retinal microperimetry: a new tool for identifying patients with type 2 diabetes at risk for developing Alzheimer disease. Diabetes 66(12):3098–3104. https://doi.org/10.2337/db17-0382
Udler MS, McCarthy MI, Florez JC, Mahajan A (2019) Genetic risk scores for diabetes diagnosis and precision medicine. Endocr Rev 40(6):1500–1520. https://doi.org/10.1210/er.2019-00088
Balboa D, Prasad RB, Groop L, Otonkoski T (2019) Genome editing of human pancreatic beta cell models: problems, possibilities and outlook. Diabetologia 62(8):1329–1336. https://doi.org/10.1007/s00125-019-4908-z
Ramamurthy M, Krishnamurthi I, Vimal S, Robinson YH (2020) Deep learning based genome analysis and NGS-RNA LL identification with a novel hybrid model. BioSystems 197:104211. https://doi.org/10.1016/j.biosystems.2020.104211
Sampaul Thomas GA, Robinson YH, Julie EG et al (2020) Diabetic retinopathy diagnostics from retinal images based on deep convolutional networks. Preprints.org. https://doi.org/10.20944/preprints202005.0493.v1
Annamalai Suresh R, Udendhran SV (2020) Deep neural networks for multimodal imaging and biomedical applications. IGI Glob. https://doi.org/10.4018/978-1-7998-3591-2
Geetha R, Sivasubramanian S, Kaliappan M et al (2019) Cervical cancer identification with synthetic minority oversampling technique and PCA analysis using random forest classifier. J Med Syst 43:286. https://doi.org/10.1007/s10916-019-1402-6
Pradeepa S, Manjula KR, Vimal S et al (2020) DRFS: detecting risk factor of stroke disease from social media using machine learning techniques. Neural Process Lett. https://doi.org/10.1007/s11063-020-10279-8
Acknowledgements
Financial support from the University of Petroleum and Energy Studies (UPES), Dehradun, India for conducting this work is gratefully acknowledged.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Chakradar, M., Aggarwal, A., Cheng, X. et al. A Non-invasive Approach to Identify Insulin Resistance with Triglycerides and HDL-c Ratio Using Machine learning. Neural Process Lett 55, 93–113 (2023). https://doi.org/10.1007/s11063-021-10461-6
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11063-021-10461-6