AUTHOR=Khan Nitasha , Raza Muhammad Amir , Mirjat Nayyar Hussain , Balouch Neelam , Abbas Ghulam , Yousef Amr , Touti Ezzeddine TITLE=Unveiling the predictive power: a comprehensive study of machine learning model for anticipating chronic kidney disease JOURNAL=Frontiers in Artificial Intelligence VOLUME=6 YEAR=2024 URL=https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2023.1339988 DOI=10.3389/frai.2023.1339988 ISSN=2624-8212 ABSTRACT=

In today's modern era, chronic kidney disease stands as a significantly grave ailment that detrimentally impacts human life. This issue is progressively escalating in both developed and developing nations. Precise and timely identification of chronic kidney disease is imperative for the prevention and management of kidney failure. Historical methods of diagnosing chronic kidney disease have often been deemed unreliable on several fronts. To distinguish between healthy individuals and those afflicted by chronic kidney disease, dependable and effective non-invasive techniques such as machine learning models have been adopted. In our ongoing research, we employ various machine learning models, encompassing logistic regression, random forest, decision tree, k-nearest neighbor, and support vector machine utilizing four kernel functions (linear, Laplacian, Bessel, and radial basis kernels), to forecast chronic kidney disease. The dataset used constitutes records from a case-control study involving chronic kidney disease patients in district Buner, Khyber Pakhtunkhwa, Pakistan. For comparative evaluation of the models in terms of classification and accuracy, diverse performance metrics, including accuracy, Brier score, sensitivity, Youden's index, and F1 score, were computed.