Document Type
Article
Publication Date
8-2025
Publisher
Institute of Electrical and Electronics Engineers
Source Publication
2025 IEEE 49th Annual Computer Software and Applications Conference
Source ISSN
2836-3795
Abstract
In this study, we develop and compare quantum and classical machine learning-based chronic kidney disease prediction models. We used the "Chronic_Kidney_Disease Data Set" of the UCI Machine Learning Repository. We performed data preprocessing and applied feature engineering techniques to select the best features. We developed two quantum machine learning-based models and two classical machine learning-based models. We used a hybrid classical-quantum environment for building quantum machine learning models. Finally, we compared the performances of all four models. We found that the Quantum Support Vector Machine performs best among the quantum models. The model’s accuracy was 95% with a k-fold cross-validation score of 94.5% and an ROC-AUC score of 0.987. Among the classical models, the Support Vector Machine showed the highest performance with an accuracy of 92.5%, a k-fold cross-validation score of 93.9%, and a ROC-AUC score of 0.974. Overall, the Quantum Support Vector Machine outperformed all other developed models in terms of accuracy and validation scores. If the quantum models can be executed in a quantum computer instead of a hybrid environment, the models will exhibit higher accuracy and faster execution time. As the models are precisely predicting chronic kidney disease with high accuracy, we believe this study will act as an inspiring framework in the less-investigated field of quantum machine learning-based chronic kidney disease prediction.
Recommended Citation
Sridevi, Parama; Upama, Paramita Basak; Rabbani, Masud; and Ahamed, Sheikh Iqbal, "Performance Comparison of Quantum and Classical Machine Learning Models for Chronic Kidney Disease Prediction" (2025). Computer Science Faculty Research and Publications. 104.
https://epublications.marquette.edu/comp_fac/104
Comments
Accepted version. 2025 IEEE 49th Annual Computer Software and Applications Conference (COMPSAC), (2025): 788-793. DOI. © 2025 Institute of Electrical and Electronics Engineers (IEEE). Used with permission.