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Particle Swarm Optimization-Tuned Deep Neural Network for Predicting Chronic Kidney Disease Onset.

Weiguo Gee
Volume 5

Abstract

Chronic Kidney Disease (CKD) is a progressive and often silent medical condition characterized by gradual loss of kidney function over time, frequently remaining undetected until advanced stages when therapeutic options become limited and the risk of cardiovascular complications, dialysis dependence, and mortality increases substantially. Early prediction of CKD onset is therefore essential for preventive nephrology, timely clinical intervention, patient monitoring, and personalized disease management. Conventional statistical risk scores and traditional machine learning approaches have shown promise in identifying high-risk patients; however, their performance may be limited by complex nonlinear interactions among clinical variables, suboptimal parameter selection, missing values, class imbalance, and variability across heterogeneous patient populations. To address these challenges, this paper proposes a Particle Swarm Optimization-tuned Deep Neural Network (PSO-DNN) framework for accurate prediction of CKD onset using structured clinical and laboratory data.

The proposed framework utilizes patient-specific information extracted from Electronic Health Records, including demographic characteristics, comorbidities, medication history, blood pressure measurements, urine analysis findings, serum creatinine, estimated glomerular filtration rate, blood urea nitrogen, albumin, hemoglobin, electrolytes, fasting blood glucose, diabetes status, hypertension status, and cardiovascular risk indicators. A comprehensive preprocessing pipeline is applied to handle missing clinical values, remove noisy or redundant attributes, normalize continuous variables, encode categorical features, and balance the dataset using class-weighted learning and resampling strategies. Feature selection and correlation analysis are incorporated to retain the most clinically meaningful predictors and reduce dimensionality before model training.

The core predictive model is based on a multilayer Deep Neural Network designed to capture complex nonlinear relationships among renal function markers, metabolic variables, and patient-level risk factors. Particle Swarm Optimization is integrated to automatically tune critical DNN hyperparameters, including learning rate, number of hidden layers, number of neurons per layer, activation function configuration, dropout rate, batch size, optimizer settings, and regularization coefficients. By replacing manual trial-and-error tuning and exhaustive grid search, PSO improves convergence efficiency, reduces computational burden, and identifies near-optimal model configurations that enhance predictive accuracy and generalization. The proposed PSO-DNN model can be evaluated using benchmark CKD datasets and real-world EHR cohorts, with performance assessed through accuracy, sensitivity, specificity, precision, F1-score, AUC-ROC, AUC-PR, calibration analysis, and cross-validation.

Experimental evaluation is expected to demonstrate that the proposed PSO-tuned DNN outperforms conventional DNN models, standalone machine learning classifiers, and non-optimized neural network baselines such as Support Vector Machine, Random Forest, Logistic Regression, XGBoost, and standard multilayer perceptron models. Particular emphasis is placed on improving sensitivity and recall for early-stage CKD onset prediction, as false-negative cases may delay preventive treatment and accelerate disease progression. To support clinical interpretability, explainability techniques such as SHAP, permutation feature importance, and patient-level risk attribution analysis are incorporated to identify the most influential predictors contributing to CKD onset. These explanations are expected to highlight clinically relevant factors such as reduced estimated glomerular filtration rate, elevated serum creatinine, albuminuria, hypertension, diabetes, anemia, abnormal blood urea nitrogen, and electrolyte imbalance.

The proposed PSO-DNN framework offers a scalable, accurate, and clinically interpretable decision-support tool for early CKD risk prediction. By combining the global search capability of Particle Swarm Optimization with the nonlinear modeling strength of deep neural networks, the system can assist clinicians in identifying high-risk patients before advanced renal impairment occurs. Its integration into hospital information systems and primary care screening workflows could support earlier referral, personalized monitoring, lifestyle intervention, medication adjustment, and improved long-term renal outcomes.

 

Keywords

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