Clinical Application of Machine Learning Models for Early-Stage Chronic Kidney Disease Detection.

Area of Science:

• Nephrology
• Artificial Intelligence
• Biomedical Informatics

Background:

• Chronic kidney disease (CKD) is a progressive condition impacting waste removal and fluid balance.
• Early detection of CKD is vital for slowing progression and enabling timely interventions.
• Machine learning (ML) offers automated solutions for disease diagnosis and prognosis.

Purpose of the Study:

• To evaluate the predictive performance of individual and ensemble ML algorithms for early CKD classification.
• To compare various ML models including Logistic Regression, LDA, QDA, Ridge Classifier, Naïve Bayes, KNN, DT, RF, SVM, and ensemble strategies.
• To assess the efficacy of ML in supporting clinical decision-making for CKD.

Main Methods:

• Utilized a clinically annotated dataset to classify patients into CKD and non-CKD groups.
• Implemented a systematic preprocessing pipeline for data preparation.
• Assessed model performance using accuracy, precision, recall, F1 score, and AUC.

Main Results:

• ML-based classifiers demonstrated high predictive accuracy in detecting CKD.
• Ensemble learning methods exhibited superior robustness and generalization compared to individual models.
• Findings indicate the potential of ML in clinical decision-making for CKD.

Conclusions:

• ML-based frameworks are effective for early CKD prediction.
• The proposed methodology provides a scalable, interpretable, and accurate clinical decision support approach.
• This approach can aid healthcare professionals in timely diagnosis and improving patient outcomes.