A Hybrid Parallel Classification Model for the Diagnosis of Chronic Kidney Disease

Chronic Kidney Disease (CKD) has become a prevalent disease nowadays, affecting people globally around the world. Accurate prediction of CKD progression over time is essential for reducing its associated mortality and morbidity rates. This paper proposes a fast, novel hybrid approach to diagnose Chronic Renal Disease. The proposed approach is based on the optimization of SVM classifier with the hybridized dimensionality reduction approach to identify the most informative parameters for CKD diagnosis. It handles the selection of features through two steps. The first one is a filter-based approach using ReliefF method to assign weights and ranks to each feature of the dataset. The second step is the dimensionality reduction of the best-selected subset by means of PCA, a feature extraction technique. For faster execution of datasets, simultaneous execution on multiple processors is employed. The proposed model achieved the highest prediction accuracy of 92.5% on the clinical CKD dataset compared to existing methods - ‘CFS+SVM’ (60.45%), ‘ReliefF + SVM’ (86%), ‘MIFS + SVM’ (56.72%), ‘ReliefF + CFS + SVM’ (54.37). The proposed work is also examined on the benchmarked Chronic Kidney Disease Dataset and achieved classification accuracy of 98.5% compared to the accuracy with other methods -‘CFS+SVM’ (92.7%), ‘ReliefF + SVM’ (89.6%), ‘MIFS + SVM’ (94.7%). The experimental outcomes positively demonstrate that the proposed hybridized model is effective in undertaking medical data classification tasks and is, therefore, a promising tool for the diagnosis of CKD patients. The proposed approach is statistically validated with the Friedman test with significant results compared to other techniques. The proposed approach also executes in the least time with improved prediction accuracy and competes with and even outperforms other methods in the literature.