Kidney Disease Prediction

Kidney Disease Prediction

P Ganga Bhavani, Assistant Professor, ECE , IARE
Ch . Venkata Sai Manoj Student ECE , IARE

Ch . Vinay Student ECE, IARE

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requirements and supports sustainable aquatic

Abstract:

Chronic Kidney Disease (CKD) is a growing public health concern, and early detection is critical for effective intervention. This study compares various machine learning models—Logistic Regression, Decision Trees, Random Forests, Support Vector Machines (SVM), and Neural Networks—to predict CKD. Results show that ensemble methods, like Random Forests, outperform individual models in terms of accuracy and robustness due to their ability to reduce overfitting. However, simpler models like Logistic Regression and Decision Trees offer greater interpretability, which is essential in clinical settings. The study underscores the trade-off between model complexity and transparency, suggesting future research should focus on integrating predictive models with Electronic Health Records (EHRs) to facilitate real-time monitoring and improve patient care.

KeyWords: Chronic Kidney Disease, Machine Learning, Random Forest, Logistic Regression, K-Nearest Neighbors, Early Detection.