2,338
Views
1
CrossRef citations to date
0
Altmetric
Research Article

LASSO-based machine learning algorithm to predict the incidence of diabetes in different stages

, , , , &
Article: 2205510 | Received 23 Dec 2022, Accepted 17 Apr 2023, Published online: 08 May 2023

References

  • Association AD. Classification and diagnosis of diabetes: standards of medical care in diabetes-2020. Diabetes Care. 2020;43(Suppl 1):S14–s31.
  • Akın S, Bölük C. Prevalence of comorbidities in patients with type-2 diabetes mellitus. Prim Care Diabetes. 2020;14(5):431–434.
  • Lai H, Huang H, Keshavjee K, et al. Predictive models for diabetes mellitus using machine learning techniques. BMC Endocr Disord. 2019;19(1):101.
  • Cahn A, Shoshan A, Sagiv T, et al. Prediction of progression from prediabetes to diabetes: development and validation of a machine learning model. Diabetes Metab Res Rev. 2020;36(2):e3252.
  • Zueger T, Schallmoser S, Kraus M, et al. Machine learning for predicting the risk of transition from prediabetes to diabetes. Diabetes Technol Ther. 2022;24(11):842–847.
  • Deberneh HM, Kim I. Prediction of type 2 diabetes based on machine learning algorithm. IJERPH. 2021;18(6):3317.
  • Shao X, Wang Y, Huang S, et al. Development and validation of a prediction model estimating the 10-year risk for type 2 diabetes in China. PLoS One. 2020;15(9):e0237936.
  • Yang J, Wang X, Jiang S. Development and validation of a nomogram model for individualized prediction of hypertension risk in patients with type 2 diabetes mellitus. Sci Rep. 2023;13(1):1298.
  • Bjørnholt JV, Erikssen G, Liestøl K, et al. Type 2 diabetes and maternal family history: an impact beyond slow glucose removal rate and fasting hyperglycemia in low-risk individuals? Results from 22.5 years of follow-up of healthy nondiabetic men. Diabetes Care. 2000;23(9):1255–1259.
  • Hyun MK, Park JH, Kim KH, et al. Incidence and risk factors for progression to diabetes mellitus: a retrospective cohort study. IJERPH. 2021;19(1):123.
  • Iheanacho CO, Osoba DO, Eze UI. Evaluation of predominant risk factors for type 2 diabetes mellitus among out-patients in two Nigerian secondary health facilities. Afr. Health Sci. 2021;21(2):693–701.
  • Grundlingh N, Zewotir TT, Roberts DJ, et al. Assessment of prevalence and risk factors of diabetes and prediabetes in South Africa. JHPN. 2022;41(1):7.
  • Rezaee M, Putrenko I, Takeh A, et al. Development and validation of risk prediction models for multiple cardiovascular diseases and type 2 diabetes. PLoS One. 2020;15(7):e0235758.
  • Wang L, Wang X, Chen A, et al. Prediction of type 2 diabetes risk and its effect evaluation based on the XGBoost model. Healthcare (Basel, Switzerland). 2020;8(3):247.
  • Ragab M, Al-Ghamdi ASA, Fakieh B, et al. Prediction of diabetes through retinal images using deep neural network. Comput Intell Neurosci. 2022;2022:7887908.
  • McEligot AJ, Poynor V, Sharma R, et al. Logistic LASSO regression for dietary intakes and breast cancer. Nutrients. 2020;12(9):2652.
  • Kang J, Choi YJ, Kim IK, et al. LASSO-based machine learning algorithm for prediction of lymph node metastasis in T1 colorectal cancer. Cancer Res Treat. 2021;53(3):773–783.
  • Choi SB, Kim WJ, Yoo TK, et al. Screening for prediabetes using machine learning models. Comput Math Methods Med. 2014;2014:618976.
  • Su B, Wang Y, Dong Y, et al. Trends in diabetes mortality in urban and rural China, 1987–2019: a joinpoint regression analysis. Front Endocrinol (Lausanne). 2021;12:777654.
  • Cole JB, Florez JC. Genetics of diabetes mellitus and diabetes complications. Nat Rev Nephrol. 2020;16(7):377–390.
  • Ceriello A, Prattichizzo F. Variability of risk factors and diabetes complications. Cardiovasc Diabetol. 2021;20(1):101.
  • Zou Q, Qu K, Luo Y, et al. Predicting diabetes mellitus with machine learning techniques. Front Genet. 2018;9:515.
  • Buch V, Varughese G, Maruthappu M. Artificial intelligence in diabetes care. Diabet Med. 2018;35(4):495–497.
  • Guo P, Zeng F, Hu X, et al. Improved variable selection algorithm using a LASSO-Type penalty, with an application to assessing hepatitis B infection relevant factors in community residents. PLoS One. 2015;10(7):e0134151.
  • Rahman M, Simmons RK, Harding AH, et al. A simple risk score identifies individuals at high risk of developing type 2 diabetes: a prospective cohort study. Fam Pract. 2008;25(3):191–196.
  • Chen L, Magliano DJ, Balkau B, et al. AUSDRISK: an Australian type 2 diabetes risk assessment tool based on demographic, lifestyle and simple anthropometric measures. Med J Aust. 2010;192(4):197–202.
  • Tsimihodimos V, Gonzalez-Villalpando C, Meigs JB, et al. Hypertension and diabetes mellitus: coprediction and time trajectories. Hypertension. 2018;71(3):422–428.
  • Alperet DJ, Lim WY, Mok-Kwee Heng D, et al. Optimal anthropometric measures and thresholds to identify undiagnosed type 2 diabetes in three major Asian ethnic groups. Obesity (Silver Spring). 2016;24(10):2185–2193.
  • Qin L, Corpeleijn E, Jiang C, et al. Physical activity, adiposity, and diabetes risk in middle-aged and older Chinese population: the Guangzhou biobank cohort study. Diabetes Care. 2010;33(11):2342–2348.
  • Bhowmik B, Siddiquee T, Mujumder A, et al. Serum lipid profile and its association with diabetes and prediabetes in a rural bangladeshi population. Int J Environ Res Public Health. 2018;15(9):1944.