A Review Prediction of Type-2 Diabetes Mellitus for Nephropathy and Neuropathy Using Machine Learning Techniques

Abstract

Diabetes mellitus (DM) is a chronic malady that is measured to be life-threatening. Diabetes Mellitus type 2 is accounts for the majority of increased risk of Neuropathy and Nephropathy. Utilizing machine learning (ML) predictive model algorithms can aid in the management of diabetes mellitus type 2 (T2DM) by lowering the risk of neuropathy and nephropathy problems in early stages. The increased screening can be avoided the loss of life our study creates predictive model to forecast the diabetes through Gradient Boosting, SVM and Random forest algorithms. The development and work by comparing the performance accomplished using various parameters and algorithms, it originates that by using a selected number of features, we can still build sufficient results.

Country : India

1 Tejashree T K2 Dr. Asha K R

  1. Research Scholar, Siddhartha Academy of Higher Education, Tumkur, India
  2. Associate Professor, Department of Computer Science, Sri Siddhartha Institute of Technology, Tumkur, India

IRJIET, Volume 8, Issue 10, October 2024 pp. 7-12

doi.org/10.47001/IRJIET/2024.810002

References

  1. Nicolucci, A., Romeo, L., Bernardini, M., Vespasiani, M., Rossi, M. C., Petrelli, M., ...& Vespasiani, G. (2022). Prediction of complications of type 2 Diabetes: A Machine learning approach. Diabetes Research and Clinical Practice, 190, 110013.
  2. Hosseini Sarkhosh, S. M., Esteghamati, A., Hemmatabadi, M., & Daraei, M. (2022).Predicting diabetic nephropathy in type 2 diabetic patients using machine learning algorithms. Journal of Diabetes & Metabolic Disorders, 21(2), 1433-1441.
  3. Shin, D. Y., Lee, B., Yoo, W. S., Park, J. W., & Hyun, J. K. (2021). Prediction of diabetic sensorimotor polyneuropathy using machine learning techniques. Journal of Clinical Medicine, 10(19), 4576.
  4. Usharani, R., & Shanthini, A. (2020). Machine learning approaches for predicting patient severity levels in T2dm complications neuropathy. European Journal of Molecular & Clinical Medicine, 7(10), 2020.
  5. Saini, D. C., Kochar, A., & Poonia, R. (2021). Clinical correlation of diabetic retinopathy with nephropathy and neuropathy. Indian Journal of Ophthalmology, 69(11), 3364-3368.
  6. Cao P, Huang B, Hong M, et al. Association of amino acids related to urea cycle with risk of diabetic nephropathy in two independent cross-sectional studies of Chinese adults. Front Endocrinol. 2022; 13:983747. doi:10.3389/fendo.2022.983747.
  7. Zhu, Y., Zhang, Y., Yang, M., Tang, N., Liu, L., Wu, J., & Yang, Y. (2024). Machine Learning-Based Predictive Modeling of Diabetic Nephropathy in Type 2 Diabetes Using Integrated Biomarkers: A Single-Center Retrospective Study. Diabetes, Metabolic Syndrome and Obesity, 1987-1997.
  8. Hosseini Sarkhosh, S. M., Hemmatabadi, M, & Esteghamati, A. (2023). Development and validation of a risk score for diabetic kidney disease prediction in type 2 diabetes patients: a machine learning approach. Journal of endocrinological investigation, 46(2), 415-423.
  9. Hearst, M. A., Dumais, S. T., Osuna, E., Platt, J., & Scholkopf, B. (1998). Support vector machines. IEEE Intelligent Systems and their applications, 13(4), 18-28.
  10. Zhang, Z., Zhao, Y., Canes, A., Steinberg, D., & Lyashevska, O. (2019). Predictive analytics with gradient boosting in clinical medicine. Annals of translational medicine, 7(7).