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Home > Archives > Volume 20, No 8 (2022) > Article

DOI: 10.14704/nq.2022.20.8.NQ44699

Chronic Disease risk prediction using squirrel search algorithm (SSA) and hybrid KNN

K. Saranyadevi, P. Rathiga


Renal failure is the last stage of chronic kidney diseases (CKD), where the renal functions is partially or totally stop working. Existing prediction model focused on the symptoms for CKD possibility that omits the high risk patients. In this study the Electronic medical record (EMR) is consider to analyze the obvious clinical symptoms to predict the renal failure using efficient feature selection approach. The SSA method is adopted to extract the most significant features from EMR and applies the machines learning algorithms to develop an end to end renal failure prediction model. Various neural networks algorithms such as Artificial Neural Networks (ANN), Multilayer Perceptron (MLP), Elman Network and Radial Basis Function Network (RBFN) are applied as classifier and compared their performance with respect to the evaluation metrics.


Renal failure prediction, machine learning, feature selection, CKD classification, Artificial neural network.

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