Abstract: Chronic kidney disease (CKD) is a prevalent and serious health condition that necessitates accurate and timely diagnosis for effective management and treatment. In this study, we explore the application of machine learning algorithms, specifically K-Neighbors Classifier, Decision Tree Classifier, Random Forest Classifier, and Extra Trees Classifier, for predicting chronic kidney diseases. The research encompasses a comprehensive analysis of a dataset containing relevant medical information such as age, blood pressure, blood glucose levels, and serum keratinize. The dataset undergoes meticulous preprocessing, including handling missing values, encoding categorical variables, and scaling numerical features. Feature selection techniques are employed to identify the most influential factors contributing to the prediction of chronic kidney diseases. Subsequently, the dataset is divided into training and testing sets to facilitate the training and evaluation of the machine learning models. The selected classifiers are trained on the training set, and their performances are evaluated on the testing set using metrics such as accuracy, precision, recall, F1 score, and ROC-AUC. The model with the highest performance is further fine-tuned through hyper parameter tuning to enhance its predictive capabilities. The outcomes of this research provide insights into the effectiveness of machine learning models in predicting chronic kidney diseases. The results underscore the importance of careful model selection, feature engineering, and hyper parameter tuning in optimizing predictive performance. The developed model holds promise for aiding healthcare professionals in early detection and management of chronic kidney diseases, potentially improving patient outcomes and reducing healthcare costs. However, the deployment of such models in real-world healthcare settings should be approached with consideration of ethical implications and domain-specific nuances.
Keywords: Kidney, Machine learning algorithms, Average accuracy, blood vessels
Cite:
Dr.V.Suganthi, M.Sabari,"Chronic Kidney Disease Detection Using Machine Learning Algorithms", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 3, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.133135.
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DOI:
10.17148/IJARCCE.2024.133135
