RESUMEN
Chronic kidney diseases (CKDs) are a significant public health issue with potential for severe complications such as hypertension, anemia, and renal failure. Timely diagnosis is crucial for effective management. Leveraging machine learning within healthcare offers promising advancements in predictive diagnostics. In this paper, we developed a machine learning-based kidney diseases prediction (ML-CKDP) model with dual objectives: to enhance dataset preprocessing for CKD classification and to develop a web-based application for CKD prediction. The proposed model involves a comprehensive data preprocessing protocol, converting categorical variables to numerical values, imputing missing data, and normalizing via Min-Max scaling. Feature selection is executed using a variety of techniques including Correlation, Chi-Square, Variance Threshold, Recursive Feature Elimination, Sequential Forward Selection, Lasso Regression, and Ridge Regression to refine the datasets. The model employs seven classifiers: Random Forest (RF), AdaBoost (AdaB), Gradient Boosting (GB), XgBoost (XgB), Naive Bayes (NB), Support Vector Machine (SVM), and Decision Tree (DT), to predict CKDs. The effectiveness of the models is assessed by measuring their accuracy, analyzing confusion matrix statistics, and calculating the Area Under the Curve (AUC) specifically for the classification of positive cases. Random Forest (RF) and AdaBoost (AdaB) achieve a 100% accuracy rate, evident across various validation methods including data splits of 70:30, 80:20, and K-Fold set to 10 and 15. RF and AdaB consistently reach perfect AUC scores of 100% across multiple datasets, under different splitting ratios. Moreover, Naive Bayes (NB) stands out for its efficiency, recording the lowest training and testing times across all datasets and split ratios. Additionally, we present a real-time web-based application to operationalize the model, enhancing accessibility for healthcare practitioners and stakeholders. Web app link: https://rajib-research-kedney-diseases-prediction.onrender.com/.
RESUMEN
In this paper, a terminal sliding mode backstepping controller (TSMBC) has been proposed for various components of a hybrid AC/DC microgrid (HADMG) to enhance its dynamic stability. The proposed control technique is employed to generate switching control signals for converters, which serve as the primary interface between the DC bus and the AC bus in a hybrid microgrid. Additionally, this technique facilitates the interface of PMSG-based wind generators, solar photovoltaic generators, and battery energy storage systems with the DC bus. Through the implementation of the composite control technique, the global stability of the microgrid is ensured by driving all the states of the HADMG associated with various components to converge towards their intended values. Afterward, the Lyapunov control theory has been used to analyze the converter and inverter's large-signal stability while ensuring the robustness of the proposed robust composite controller. Finally, an extensive simulation study was conducted on a hybrid microgrid to verify the efficacy of the designed controller in maintaining power balance amidst variations in the system's operational regimes. Moreover, the effectiveness of the controller's practical implementation is confirmed by real-time processor-in-the-loop analysis. Simulation results clearly show that the proposed TSMBC improves the overall dynamic performance of the hybrid microgrid with less overshoot (0%) and settling time (110 ms) in DC bus voltage when compared to the existing sliding mode controller.
