Abstract
Kidney disease is a global health problem, with an increasing number of new patients yearly; the same happens in Panama. The demand for dialysis, hemodialysis, and other costly treatments is also rising. In most cases, the problem is detected at an advanced stage; therefore, it is essential to create a model that could warn the medical doctor about the possibility of disease or see early signs of renal disease. This work presents the first experiences of analyzing, developing, and comparing three machine learning algorithm prototype models that could early alert the medical doctor of possible chronic kidney disease using an open-access dataset in the patient being the Naïve Bayes model the more accurate. Collecting a Panamanian chronic kidney disease dataset and training the models with national patient data is strongly suggested.
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Nunez, I., Navarro, N., Saldana-Barrios, J.J. (2023). First Experiences in the Process of Developing a Low-Cost Machine Learning Prototype Model Using an Open Access Dataset of Chronic Kidney Diseases – A Case of Study. In: Bravo, J., Ochoa, S., Favela, J. (eds) Proceedings of the International Conference on Ubiquitous Computing & Ambient Intelligence (UCAmI 2022). UCAmI 2022. Lecture Notes in Networks and Systems, vol 594. Springer, Cham. https://doi.org/10.1007/978-3-031-21333-5_18
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