A Review on Kidney Failure Prediction Using Machine Learning Models

End-stage renal disease (ESRD), commonly known as kidney failure, is a critical medical condition that has a significant impact on global health. Early detection of kidney failure is crucial in preventing and managing this condition. In recent years, machine learning (ML) models have emerged as promising tools for predicting kidney failure, offering the potential to improve patient outcomes through timely intervention. This comprehensive review provides an overview of the current state of research on kidney failure prediction using various ML models. The review begins by presenting an overview of kidney failure, its prevalence, and the challenges associated with its early detection. It then delves into the role of ML in healthcare and specifically focuses on its application in predicting kidney failure. The discussion encompasses a wide range of ML techniques, including logistic regression, decision trees, support vector machines, and deep learning. The review analyzes key studies and methodologies employed in predicting kidney failure, highlighting the strengths and limitations of different ML approaches. It emphasizes the importance of feature selection, data preprocessing, and model evaluation in enhancing the accuracy and reliability of predictions. Furthermore, it addresses the issue of data imbalance, a common challenge in medical datasets, and explores strategies to mitigate its impact on model performance. In addition to summarizing existing research, the review identifies current gaps in the literature and suggests avenues for future research. This includes the exploration of novel data sources, the integration of multi-modal data, and the development of interpretable models that can assist healthcare professionals in making informed decisions. Overall, this review serves as a valuable resource for researchers, clinicians, and healthcare professionals interested in the application of ML models for kidney failure prediction. By synthesizing the current state of knowledge, it provides insights into the potential of ML models to improve patient outcomes and highlights areas for further research.