Hybrid Diagnostic Model for Kidney Disease Prediction Using Data Mining Techniques
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Abstract
This paper proposes and evaluates a hybrid diagnostic model for kidney disease prediction using data mining techniques, as a potential solution to the current problems facing existing models. The model is based on a combination of Artificial Neural networks, and Support Vector Machines algorithms, trained on the symptoms and patient-reported data. The dataset contains 400 instances which are based on 25 attributes, retrieved from the University of California, Irvine machine learning repository chronic_Kidney_Disease Dataset. WEKA toolkit was used to preprocess the dataset, apply the data mining algorithms, analysis and evaluation. The evaluation of the model using a 10-fold cross-validation technique shows that the hybrid model outperforms both individual ANN and SVM algorithms in terms of accuracy and stability. Also, it shows that the hybrid of ANN-SVM has the lowest MAE, RMSE, and RRSE in classifying the CKD dataset, followed by ANN having lower MAE compared to ANN, but ANN had lower RMSE, RAE, and RRSE than the SVM. The model described in this paper is an efficient tool for predicting kidney disease, as it can be used to diagnose a variety of kidney diseases and can potentially reduce the number of unnecessary tests that are currently conducted to diagnose kidney disease. In addition, the model can be used to ensure that patients receive the most appropriate medical screenings and treatments for kidney diseases. In conclusion, the proposed hybrid diagnostic model for kidney disease prediction using data mining techniques has shown that it is a viable and promising alternative to existing methods.
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