Predicting Patients with Renal Failure using Neural Networks
DOI:
https://doi.org/10.59992/IJCI.2026.v5n6p5Keywords:
Prediction, Patients, Renal Failure, Neural NetworksAbstract
Artificial neural network considered as most important in statistic and artificial intelligent that reflex important improvement for future prediction a series of time data from 1999 to 2018, which represent 20 series of time data represent renal failure patients men and women, bath. Results of the research reach predictions in three years represented in the following years (2019-2020-2021). Through results the predictions values have best used way from traditional used way in prediction previously. The documents loosed on researchers from Ibn Seena Hospital in Nenavah Governorate in renal failure.
References
1. Amin Bey, Azza (2005) “The use of neural networks in predicting time series by applying to the initialization of electricity in the city of Mosul,” an unpublished master’s thesis, College of Computer Science and Mathematics, University of Mosul.
2. Qasim, Omar; Muhammad, Esraa (2013) "An Analytical Mathematical Study of Artificial Neural Network Algorithms in Fitting a Model for Medical Diagnosis" Proceedings of the Fifth Conference on Information Technology.
3. Makridakis, Spyros, wright, Steven C., & Hyndan, Rob (1998) “For a casting Method and Application”, 3 Ed. John Wiley Sane. Ine, USA., 1998.
4. Pranati Sahu; “The Evolution of Time Series Analysis: Beyond Traditional Forecasting” International Journal of Computational and Experimental Science and ENgineering (IJCESEN), Vol. 11-No.4 (2025) pp. 8600-8608.
5. Sumathi, S, Surekhap; (2010) ”Computation Intelligence paradims Theory and Application Using MATLAB, by Taylor and Francis Group, LLCCRC press is an imprint of Taylor Francis Group, an business.
6. Jie Zheng, Shunping Ouyang, Jian Kang, and Youcun Xiao, "Prediction Method Based on Time Series", Highlights in Science, Engineering and Technology, Volume 47 (2023).
7. Ajanwachuku Nwagu Chima and Austine E. Duroha, "Artificial Neural Network Application in Prediction – A Review", African Journal of Computing & ICT Reference Format, Vol. 12, No. 4, December 2019, pp. 75 – 85.
8. Stephen Haben, Marcus Voss and William Holderbaum (2023) “Core Concepts and Methods in Load Forecasting with Applications in Distribution Networks” ISBN 978-3-031-27851-8 ISBN 978-3-031-27852-5 (eBook).
