Utilizing Artificial Neural Networks for Predicting Chronic Kidney Disease
DOI:
https://doi.org/10.63503/acset.92Keywords:
Chronic kidney disease, Artificial neural network, Estimated Glomerular filtration rate, Data preprocessing, Correlation-based feature subset selectionAbstract
The goal of the Chronic Kidney Disease Prediction project is to create a model based on machine learning that can predict a person's chance of getting chronic kidney disease by utilizing a variety of demographic data, medical history, and test results. Globally, the prevalence of chronic kidney disease is increasing, and early detection is crucial for optimal treatment and therapy. The suggested model will be trained on a large patient dataset and will use a range of machine learning methods to evaluate the data and predict the likelihood that an individual will develop chronic kidney disease. This research uses an artificial neural network-based method for the forecasting of chronic illness. When an artificial neural network is trained on a dataset with several informative properties, backpropagation procedures are employed for patients. The output of this neural network enables us to assess whether a patient has persistent renal disease. Artificial neural networks may predict renal illness more accurately than alternative machine learning algorithms.
References
[1] Vijayarani, S., S. Dhayanand, and M. Phil. "Kidney disease prediction using SVM and ANN algorithms." International Journal of Computing and Business Research (IJCBR) 6, no. 2 (2015): 112.
[2] Chittora, Pankaj, Sandeep Chaurasia, Prasun Chakrabarti, Gaurav Kumawat, Tulika Chakrabarti, Zbigniew Leonowicz, Michał Jasiński et al. "Prediction of chronic kidney disease-a machine learning perspective." IEEE Access 9 (2021): 17312-17334.
[3] Chittora, Pankaj, Sandeep Chaurasia, Prasun Chakrabarti, Gaurav Kumawat, Tulika Chakrabarti, Zbigniew Leonowicz, Michał Jasiński et al. "Prediction of chronic kidney disease-a machine learning perspective." IEEE Access 9 (2021): 17312-17334.
[4] Singh, Vijendra, Vijayan K. Asari, and Rajkumar Rajasekaran. "A deep neural network for early detection and prediction of chronic kidney disease." Diagnostics 12, no. 1 (2022): 116.
[5] Zhang, Hanyu, Che-Lun Hung, William Cheng-Chung Chu, Ping-Fang Chiu, and Chuan Yi Tang. "Chronic kidney disease survival prediction with artificial neural networks." In 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1351-1356. IEEE, 2018.
[6] Dubey, Gaurav, Yashdeep Srivastava, Aman Verma, and Shriyansh Rai. "Chronic Kidney Disease
[7] Prediction Using Artificial Neural Network." In Proceedings of International Conference on Big Data, Machine Learning and their Applications: ICBMA 2019, pp. 395-401. Springer Singapore, 2021.
[8] Revathy, S., B. Bharathi, P. Jeyanthi, and M. Ramesh. "Chronic kidney disease prediction using machine learning models." International Journal of Engineering and Advanced Technology 9, no. 1 (2019): 6364-6367.
[9] Debal, Dibaba Adeba, and Tilahun Melak Sitote. "Chronic kidney disease prediction using machine learning techniques." Journal of Big Data 9, no. 1 (2022): 1-19.
[10] Ghosh, Pronab, FM Javed Mehedi Shamrat, Shahana Shultana, Saima Afrin, Atqiya Abida Anjum, and Aliza Ahmed Khan. "Optimization of prediction method of chronic kidney disease using machine learning algorithm." In 2020 15th international joint symposium on artificial intelligence and natural language processing (iSAI-NLP), pp. 1-6. IEEE, 2020.
[11] Sinha, Parul, and Poonam Sinha. "Comparative study of chronic kidney disease prediction using KNN and SVM." International Journal of Engineering Research and Technology 4, no. 12 (2015): 608-12.
[12] Schena, Francesco Paolo, Vito Walter Anelli, Daniela Isabel Abbrescia, and Tommaso Di Noia. "Prediction of chronic kidney disease and its progression by artificial intelligence algorithms." Journal of Nephrology 35, no. 8 (2022): 1953-1971.
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