Abstract
Chronic Kidney Disease (CKD) is a global public health problem and one of the world’s most overlooked chronic diseases. Once most CKD patients have diabetes or hypertension, the disease affects global morbidity and mortality, and care can be costly. However, through inexpensive approaches, CKD may be avoided or postponed. When the prediction is accurate, it is possible to improve quality control in diagnosing and treating CKD. We propose in this work a Random Forest (RF) classifier combined with the imputation of missing data to predict CKD stages. We tested four different scenarios, and our findings show that the RF algorithm provides higher accuracy on classification and prediction performance for determining the severity stage in CKD.
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Scoralick, J.P., Iwashima, G.C., Colugnati, F.A.B., Goliatt, L., Capriles, P.V.S.Z. (2021). A Random Forest Classifier Combined with Missing Data Strategies for Predicting Chronic Kidney Disease Stages. In: Pan, I., Mukherjee, A., Piuri, V. (eds) Proceedings of Research and Applications in Artificial Intelligence. Advances in Intelligent Systems and Computing, vol 1355. Springer, Singapore. https://doi.org/10.1007/978-981-16-1543-6_24
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DOI: https://doi.org/10.1007/978-981-16-1543-6_24
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