Hybrid Machine Learning Classifier Models for Kidney Disease Detection

Abstract

Detecting kidney disease at an early stage is crucial for timely intervention and improved patient outcomes. In recent years, machine learning classifiers have shown promise in enhancing the accuracy and efficiency of diagnosing kidney disease. This research paper delves into the comparative analysis of Support Vector Machine (SVM) classifier, Random Forest classifiers, and a Hybrid model combining SVM and Decision Tree for kidney disease detection. The introduction of each classifier, including SVM's classification mechanism, advantages, and preferred usage scenarios, as well as Random Forest's approach to combating overfitting through ensemble learning and parameter tuning considerations, sets the stage for a comprehensive evaluation. Additionally, exploring the benefits, challenges, and synergistic strengths of a Hybrid model in leveraging SVM's robustness and Decision Tree's interpretability is essential for understanding its potential in kidney disease detection. By investigating the common features utilized for kidney disease detection and assessing the accuracy and implications of early detection using machine learning models, this paper aims to contribute to the advancement of medical diagnostics. Furthermore, the study will evaluate and compare the performance of SVM, Random Forest, and Hybrid classifiers, examining the metrics employed for model effectiveness assessment and addressing any limitations or biases inherent in interpreting the results for kidney disease detection. Through this research, we aim to provide valuable insights into the application of machine learning classifiers in medical diagnostics, particularly in the context of kidney disease detection.