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Early diagnosis and prediction of chronic kidney disease (CKD) progress within a given duration are critical to ensure personalized treatment, which could improve patients’ quality of life and prolong survival time. In this paper, we employ experiential analysis of ML techniques for classifying the kidney patient dataset as CKD or NOTCKD. Feature selection is a crucial step in ML, as it helps extract a subset of important features from the dataset. This process offers several benefits, including improved prediction accuracy, reduced model complexity, and enhanced interpretability. In this study, we utilized the Tabu Search with Boruta Algorithm feature selection technique, which leverages random shadow features and an ML model. Boruta compares the importance of each feature to that of the shadow features iteratively, categorizing features as confirmed, tentative, or rejected based on their significance. Ultimately, Boruta provides a subset of the most significant features from the dataset. Feature selection is performed using the Tabu Search with Boruta algorithm, and the model is developed using ML algorithms. The proposed model was validated on the UCI CKD dataset, achieving outstanding performance with 99.75 % accuracy. This study introduces a novel Deep learning network based on PointRCNN (FuDNN) Architecture for CKD early detection and prediction. More processing attributes of characteristics chosen to indicate a kidney issue are extracted by PointRCNN. Accuracy, sensitivity, specificity, AUC score and F1 score are the performance metrics for the suggested CKD classification approach. Additional experimental findings demonstrate that the suggested method produces a better categorization of CKD than the present state-of-the-art method. |
