Research Article
BibTex RIS Cite

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

Year 2024, Volume: 45 Issue: 4, 769 - 776, 30.12.2024

Bugce Eminaga Tatlicioglu

https://doi.org/10.17776/csj.1441313

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

Neural network Runge kutta Multiplicative calculus Long Short-Term Memory Dabetes.

References

  • [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] 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] 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] 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] 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] 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] 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] 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.
  • [9] Maniruzzaman M., Rahman M. J., Al-MehediHasanM., Suri, H. S., Abedin, M. M., El-Baz A., Suri J. S., Accurate diabetes risk stratification using machine learning: role of missing value and outliers. Journal of medical systems, 42 (2018) 1-17.
  • [10] Brahim-Belhouari S., Bermak A., Gaussian process for nonstationary time series prediction, Computational Statistics & Data Analysis, 47(4) (2004) 705-712.
  • [11] Cortes C., Vapnik V., Support-vector networks, Machine learning, 20 (1995) 273-297.
  • [12] Kégl B., The return of AdaBoost. MH: multi-class Hamming trees, (2013) arXiv preprint arXiv:1312.6086.
  • [13] Tabaei B. P., Herman W. H., A multivariate logistic regression equation to screen for diabetes: development and validation, Diabetes Care, 25(11) (2002) 1999-2003.
  • [14] Jenhani I., Amor N. B., Elouedi Z., Decision trees as possibilistic classifiers, International journal of approximate reasoning, 48(3) (2008) 784-807.
  • [15] Qawqzeh Y. K., Bajahzar A. S., Jemmali M., Otoom M. M., Thaljaoui A., Classification of diabetes using photoplethysmogram (PPG) waveform analysis: logistic regression modeling. BioMed Research International, (2020).
  • [16] Pethunachiyar G. A., Classification of diabetes patients using kernel based support vector machines. In 2020 International Conference on Computer Communication and Informatics (ICCCI), (2020) 1-4).
  • [17] Abdu-Allah Z. M., Mahmood O. T., AL-Naib A. M. I., Photovoltaic Battery Charging System Based on PIC16F877A Microcontroller, International Journal of Engineering and Advanced Technology, 3(4) (2014) 55-59.
  • [18] Kumari S., Kumar D., Mittal M., An ensemble approach for classification and prediction of diabetes mellitus using soft voting classifier. International Journal of Cognitive Computing in Engineering, 2 (2021) 40-46.
  • [19] Hussain A., Naaz S., Prediction of diabetes mellitus: comparative study of various machine learning models, In International Conference on Innovative Computing and Communications: Proceedings of ICICC 2020 Springer Singapore, 2 (2021) 103-115.
  • [20] Hussain A., Naaz, S., Prediction of diabetes mellitus: comparative study of various machine learning models, In International Conference on Innovative Computing and Communications: Proceedings of ICICC 2020 Springer Singapore, 2 (2020) 103-115.
  • [21] Bashirov A. E., Mustafa R. I. Z. A., On complex multiplicative differentiation, TWMS Journal of applied and engineering mathematics, 1(1) (2011) 75-85.
  • [22] Bashirov A. E., Norozpour S., On complex multiplicative integration, TWMS Journal of Applied and Engineering Mathematics, 7(1) (2017) 82-93.
  • [23] Uzer A., Multiplicative type complex calculus as an alternative to the classical calculus, Computers & Mathematics with Applications, 60(10) (2010) 2725-2737.
  • [24] Babacan Y., Kaçar F., Memristor emulator with spike-timing-dependent-plasticity, AEU-International Journal of Electronics and Communications, 73(2017) 16-22.
  • [25] Bashirov A. E., Bashirova G., Dynamics of literary texts and diffusion, Online Journal of Communication and Media Technologies, 1(3) (2011) 60-82.
  • [26] Wang Y., Liao X., Stability analysis of multimode oscillations in three coupled memristor-based circuits, AEU-International Journal of Electronics and Communications, 70(12) (2016) 1569-1579.
  • [27] Aniszewska D., Multiplicative runge–kutta methods, Nonlinear Dynamics, 50(1-2) (2007) 265-272.
  • [28] Aniszewska D., Multiplicative runge–kutta methods, Nonlinear Dynamics, 50(1-2) (2007) 265–272,.
  • [29] Dileep P., Rao K. N., Bodapati P., Gokuruboyina S., Peddi R., Grover A., Sheetal A., An automatic heart disease prediction using cluster-based bi-directional LSTM (C-BiLSTM) algorithm, Neural Computing and Applications, 35(10) (2023) 7253-7266.
  • [30] Srinivasu P. N., SivaSai J. G., Ijaz M. F., Bho, A. K., Kim W., Kang J. J., Classification of skin disease using deep learning neural networks with MobileNet V2 and LSTM, Sensors, 21(8) (2021) 2852.
  • [31] Dua M., Makhija D., Manasa P. Y. L., Mishra P., A CNN–RNN–LSTM based amalgamation for Alzheimer’s disease detection, Journal of Medical and Biological Engineering, 40(5) (2020) 688-706.
  • [32] Bashirov A. E., Mustafa R. I. Z. A., On complex multiplicative differentiation, TWMS Journal of applied and engineering mathematics, 1(1) (2011) 75-85.
  • [33] Riza M., Aktöre H., (The Runge–Kutta method in geometric multiplicative calculus, LMS Journal of Computation and Mathematics, 18(1) (2015) 539-554.
  • [34] Hochreiter S., Schmidhuber J., Long short-term memory, Neural computation, 9(8) (1997) 1735-1780.
  • [35] Gers F. A., Schmidhuber J., Cummins F., Continual prediction using LSTM with forget gates, In Neural Nets WIRN Vietri-99: Proceedings of the 11th Italian Workshop on Neural Nets, Vietri Sul Mare, Salerno, Italy, 20–22 May 1999, Springer London, (1999) 133-138.
  • [36] Gers F. A., Schmidhuber J., Cummins F., Learning to forget: Continual prediction with LSTM, Neural computation, 12(10) (2000) 2451-2471.
  • [37] Gers F. A., Schmidhuber E., LSTM recurrent networks learn simple context-free and context-sensitive languages, IEEE transactions on neural networks, 12(6) (2001) 1333-1340.

Year 2024, Volume: 45 Issue: 4, 769 - 776, 30.12.2024

Bugce Eminaga Tatlicioglu

https://doi.org/10.17776/csj.1441313

Abstract

References

  • [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] 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] 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] 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] 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] 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] 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] 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.
  • [9] Maniruzzaman M., Rahman M. J., Al-MehediHasanM., Suri, H. S., Abedin, M. M., El-Baz A., Suri J. S., Accurate diabetes risk stratification using machine learning: role of missing value and outliers. Journal of medical systems, 42 (2018) 1-17.
  • [10] Brahim-Belhouari S., Bermak A., Gaussian process for nonstationary time series prediction, Computational Statistics & Data Analysis, 47(4) (2004) 705-712.
  • [11] Cortes C., Vapnik V., Support-vector networks, Machine learning, 20 (1995) 273-297.
  • [12] Kégl B., The return of AdaBoost. MH: multi-class Hamming trees, (2013) arXiv preprint arXiv:1312.6086.
  • [13] Tabaei B. P., Herman W. H., A multivariate logistic regression equation to screen for diabetes: development and validation, Diabetes Care, 25(11) (2002) 1999-2003.
  • [14] Jenhani I., Amor N. B., Elouedi Z., Decision trees as possibilistic classifiers, International journal of approximate reasoning, 48(3) (2008) 784-807.
  • [15] Qawqzeh Y. K., Bajahzar A. S., Jemmali M., Otoom M. M., Thaljaoui A., Classification of diabetes using photoplethysmogram (PPG) waveform analysis: logistic regression modeling. BioMed Research International, (2020).
  • [16] Pethunachiyar G. A., Classification of diabetes patients using kernel based support vector machines. In 2020 International Conference on Computer Communication and Informatics (ICCCI), (2020) 1-4).
  • [17] Abdu-Allah Z. M., Mahmood O. T., AL-Naib A. M. I., Photovoltaic Battery Charging System Based on PIC16F877A Microcontroller, International Journal of Engineering and Advanced Technology, 3(4) (2014) 55-59.
  • [18] Kumari S., Kumar D., Mittal M., An ensemble approach for classification and prediction of diabetes mellitus using soft voting classifier. International Journal of Cognitive Computing in Engineering, 2 (2021) 40-46.
  • [19] Hussain A., Naaz S., Prediction of diabetes mellitus: comparative study of various machine learning models, In International Conference on Innovative Computing and Communications: Proceedings of ICICC 2020 Springer Singapore, 2 (2021) 103-115.
  • [20] Hussain A., Naaz, S., Prediction of diabetes mellitus: comparative study of various machine learning models, In International Conference on Innovative Computing and Communications: Proceedings of ICICC 2020 Springer Singapore, 2 (2020) 103-115.
  • [21] Bashirov A. E., Mustafa R. I. Z. A., On complex multiplicative differentiation, TWMS Journal of applied and engineering mathematics, 1(1) (2011) 75-85.
  • [22] Bashirov A. E., Norozpour S., On complex multiplicative integration, TWMS Journal of Applied and Engineering Mathematics, 7(1) (2017) 82-93.
  • [23] Uzer A., Multiplicative type complex calculus as an alternative to the classical calculus, Computers & Mathematics with Applications, 60(10) (2010) 2725-2737.
  • [24] Babacan Y., Kaçar F., Memristor emulator with spike-timing-dependent-plasticity, AEU-International Journal of Electronics and Communications, 73(2017) 16-22.
  • [25] Bashirov A. E., Bashirova G., Dynamics of literary texts and diffusion, Online Journal of Communication and Media Technologies, 1(3) (2011) 60-82.
  • [26] Wang Y., Liao X., Stability analysis of multimode oscillations in three coupled memristor-based circuits, AEU-International Journal of Electronics and Communications, 70(12) (2016) 1569-1579.
  • [27] Aniszewska D., Multiplicative runge–kutta methods, Nonlinear Dynamics, 50(1-2) (2007) 265-272.
  • [28] Aniszewska D., Multiplicative runge–kutta methods, Nonlinear Dynamics, 50(1-2) (2007) 265–272,.
  • [29] Dileep P., Rao K. N., Bodapati P., Gokuruboyina S., Peddi R., Grover A., Sheetal A., An automatic heart disease prediction using cluster-based bi-directional LSTM (C-BiLSTM) algorithm, Neural Computing and Applications, 35(10) (2023) 7253-7266.
  • [30] Srinivasu P. N., SivaSai J. G., Ijaz M. F., Bho, A. K., Kim W., Kang J. J., Classification of skin disease using deep learning neural networks with MobileNet V2 and LSTM, Sensors, 21(8) (2021) 2852.
  • [31] Dua M., Makhija D., Manasa P. Y. L., Mishra P., A CNN–RNN–LSTM based amalgamation for Alzheimer’s disease detection, Journal of Medical and Biological Engineering, 40(5) (2020) 688-706.
  • [32] Bashirov A. E., Mustafa R. I. Z. A., On complex multiplicative differentiation, TWMS Journal of applied and engineering mathematics, 1(1) (2011) 75-85.
  • [33] Riza M., Aktöre H., (The Runge–Kutta method in geometric multiplicative calculus, LMS Journal of Computation and Mathematics, 18(1) (2015) 539-554.
  • [34] Hochreiter S., Schmidhuber J., Long short-term memory, Neural computation, 9(8) (1997) 1735-1780.
  • [35] Gers F. A., Schmidhuber J., Cummins F., Continual prediction using LSTM with forget gates, In Neural Nets WIRN Vietri-99: Proceedings of the 11th Italian Workshop on Neural Nets, Vietri Sul Mare, Salerno, Italy, 20–22 May 1999, Springer London, (1999) 133-138.
  • [36] Gers F. A., Schmidhuber J., Cummins F., Learning to forget: Continual prediction with LSTM, Neural computation, 12(10) (2000) 2451-2471.
  • [37] Gers F. A., Schmidhuber E., LSTM recurrent networks learn simple context-free and context-sensitive languages, IEEE transactions on neural networks, 12(6) (2001) 1333-1340.
There are 37 citations in total.

Details

Primary Language English
Subjects Experimental Mathematics, Biological Mathematics, Applied Mathematics (Other)
Journal Section Natural Sciences
Authors

Bugce Eminaga Tatlicioglu 0000-0001-8854-4464

Publication Date December 30, 2024
Submission Date February 22, 2024
Acceptance Date October 24, 2024
Published in Issue Year 2024Volume: 45 Issue: 4

Cite

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
 Download Cover Image