Research Article

Analyzing Diabetic Dynamics with MRK4, and LSTM Techniques with Multiplicative Calculus

Volume: 45 Number: 4 December 30, 2024
EN

Analyzing Diabetic Dynamics with MRK4, and LSTM Techniques with Multiplicative Calculus

Abstract

This study compares the use of Long Short-Term Memory (LSTM) networks for predictive modeling with multiplicative calculus. We evaluate and quantitatively analyze both methodologies to determine their prediction performance. While LSTM networks are investigated for them power to learn and generalize patterns, the multiplicative calculus technique is analyzed for its ability to grasp complex connections within the data. This study attempts to shed light on the efficacy of each approach by carefully analyzing error measures including mean squared error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE). The results aid in the comprehending of the subtleties related to LSTM networks and multiplicative calculus, assisting practitioners and researchers in choosing the best method for tasks involving predictive modeling.

Keywords

References

  1. [1] Emerging Risk Factors Collaboration, Diabetes mellitus, fasting blood glucose concentration, and risk of vascular disease: a collaborative meta-analysis of 102 prospective studies, The lancet, 375(9733) (2010) 2215-222
  2. [2] Ramsingh J., & Bhuvaneswari V. (2021). An efficient map reduce-based hybrid NBC-TFIDF algorithm to mine the public sentiment on diabetes mellitus–a big data approach, Journal of King Saud University-Computer and Information Sciences, 33(8) 1018-1029.
  3. [3] Artzi N. S., Shilo, S., Hadar E., Rossman H., Barbash-Hazan S., Ben-Haroush A., ... , Segal E., Prediction of gestational diabetes based on nationwide electronic health records. Nature medicine, 26(1) (2020), 71-76.
  4. [4] Misra A., Gopalan H., Jayawardena R., Hills A. P., Soares M., Reza‐Albarrán A. A., Ramaiya K. L., Diabetes in developing countries. Journal of diabetes, 11(7) (2019) 522-539.
  5. [5] Edwards M. S., Wilson D. B., Craven T. E., Stafford J., Fried L. F., Wong T. Y., ... , Hansen K. J., Associations between retinal microvascular abnormalities and declining renal function in the elderly population: the Cardiovascular Health Study. American journal of kidney diseases, 46(2) (2005) 214-224.
  6. [6] Saeedi P., Petersohn I., Salpea P., Malanda B., Karuranga S., Unwin N., et all., IDF Diabetes Atlas CommitteeGlobal and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, Diabetes research and clinical practice, 157 (2019) 107843.
  7. [7] Vaishali R., Sasikala R., Ramasubbareddy S., Remya S., Nalluri SGenetic algorithm based feature selection and MOE Fuzzy classification algorithm on Pima Indians Diabetes dataset. In 2017 international conference on computing networking and informatics (ICCNI), (2017) 1-5.
  8. [8] Cho N. H., Shaw J. E., Karuranga S., Huang Y., da Rocha Fernandes J. D., Ohlrogge,A. W., Malanda, B. I. D. F., IDF Diabetes Atlas: Global estimates of diabetes prevalence for 2017 and projections for 2045, Diabetes research and clinical practice, 138 (2018) 271-281.

Details

Primary Language

English

Subjects

Experimental Mathematics , Biological Mathematics , Applied Mathematics (Other)

Journal Section

Research Article

Publication Date

December 30, 2024

Submission Date

February 22, 2024

Acceptance Date

October 24, 2024

Published in Issue

Year 1970 Volume: 45 Number: 4

APA
Eminaga Tatlicioglu, B. (2024). Analyzing Diabetic Dynamics with MRK4, and LSTM Techniques with Multiplicative Calculus. Cumhuriyet Science Journal, 45(4), 769-776. https://doi.org/10.17776/csj.1441313

As of 2026, Cumhuriyet Science Journal will be published in six issues per year, released in February, April, June, August, October, and December