Performance Evaluation and Comparative Analysis of Several Machine Learning Classification Techniques Using a Data-driven Approach in Predicting Renal Failure

Abstract

Abstract: Renal failure is characterized by progressive kidney function loss over time. It is a serious medical condition that affects millions of people worldwide. It is caused by the inability of the kidneys to properly filter waste and excess fluids from the blood. Renal failure can be a consequence of chronic kidney disease. Chronic kidney disease is a long-term condition that causes the kidneys to gradually lose function over time. If chronic kidney disease is not adequately managed, the kidney’s function may continue to decline, leading to renal failure. It is essential to monitor and manage chronic kidney disease to prevent renal failure from developing. This research paper presents an approach for predicting renal failure using several machine-learning classification techniques. The study evaluates the performance of various classifiers such as Decision Tree, Naive Bayes, Extreme Gradient Boosting, Logistic Regression, and Support Vector Machines using various evaluation metrics. The performance of these classifiers is evaluated using various metrics such as accuracy, precision, recall, and F1-score. This proposed method can be useful for early diagnosis and treatment of renal failure, thus reducing the complications and costs associated with the disease. By comparing and evaluating the performance of these models, we aim to identify the most effective approach for predicting renal failure and provide valuable insights for clinical practice.