Clinical risk assessment of chronic kidney disease patients using genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8081

@Article{kumar2021clinical,

• author = "Arvind Kumar and Nishant Sinha and Arpit Bhardwaj and Shivani Goel",
• title = "Clinical risk assessment of chronic kidney disease patients using genetic programming",
• journal = "Computer Methods in Biomechanics and Biomedical Engineering",
• year = "2022",
• volume = "25",
• number = "8",
• pages = "887--895",
• note = "Online since 02 Nov 2021",
• keywords = "genetic algorithms, genetic programming, chronic kidney disease, CKD, clinical risk assessment, imbalanced classification, fitness function",
• publisher = "Taylor \& Francis",
• DOI = "doi:10.1080/10255842.2021.1985476",
• size = "9 pages",
• abstract = "Chronic kidney disease (CKD) is one of the serious health concerns in the twenty-first century. CKD impacts over 37 million Americans. By applying machine learning (ML) techniques to clinical data, CKD can be diagnosed early. This early detection of CKD can prevent numerous loss of life. In this work, clinical data set of 400 patients, available on the UCI repository, are taken. Unfortunately, this data set does not have an equal distribution of CKD and Non-CKD samples. This imbalanced nature of data highly influences the learning capabilities of classifiers. Genetic Programming (GP) is an ML technique based on the evolution of species. GP with standard fitness function, also impacted by this imbalanced nature of data. A new Euclidean distance-based fitness function in GP is proposed to handle this imbalanced nature of the data set. To compare the robustness of the proposed work, other classification techniques, K-nearest neighborhood (KNN), KNN with particle swarm optimization (PSO), and GP with the standard fitness function, is also applied. For ten-fold cross-validation, the KNN shows an accuracy of 83.54percent with an AUC value of 0.69, the PSO-KNN shows an accuracy of 96.79percent with an AUC value of 0.94, and the GP, with the newly proposed fitness function, supersedes KNN and PSO-KNN and shows the accuracy of 99.33percent with an AUC value of 0.99.",

}

Genetic Programming entries for Arvind Kumar Nishant Sinha Arpit Bhardwaj Shivani Goel

Citations