Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2025 Apr 17:11:e2789.
doi: 10.7717/peerj-cs.2789. eCollection 2025.

Variational quantum classifier-based early identification and classification of chronic kidney disease using sparse autoencoder and LASSO shrinkage

P Parthasarathi  1 , Haya Mesfer Alshahrani  2 , K Venkatachalam  3 , Jaehyuk Cho  4
Affiliations

Variational quantum classifier-based early identification and classification of chronic kidney disease using sparse autoencoder and LASSO shrinkage

P Parthasarathi et al. PeerJ Comput Sci. .

Abstract

The two leading causes of chronic kidney disease (CKD) are excessive blood pressure and diabetes. Researchers worldwide utilize the rate of globular filtration and kidney inflammation biomarkers to identify chronic kidney disease that gradually reduces renal function. The mortality rate for CKD is high, and thus, a person with this illness is more likely to pass away at a younger age. Healthcare professionals must diagnose the various illnesses connected to this deadly disease as promptly as possible to lighten the impact of CKD. A quantum machine learning (QML) based technique is presented in this research to help with the early diagnosis and prognosis of CKD. The proposed research comprises four phases: data pre-processing, data augmentation, feature selection, and classification. In the first phase, Kalman filter and data normalization techniques are applied to handle the missing and noisy data. In the second phase, data augmentation uses sparse autoencoders to balance the data for smaller classes. In the third phase, LASSO shrinkage is used to select the significant features in the dataset. Variational Quantum classifiers, a supervised QML technique, are employed in the classification phase to classify chronic kidney diseases. The proposed system has been evaluated on the UCI dataset, which comprises 400 CKD patients in the early stages with 25 attributes. The suggested system was assessed using F1-score, precision, recall, and accuracy as evaluation metrics. With a 99.2% classification accuracy, it was found that this model performed better than the other traditional classifiers used for chronic kidney disease classification.

Keywords: Autoencoder; Chronic kidney disease; Deep learning; LASSO shrinkage; Quantum classifier.

PubMed Disclaimer

Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Figure 1
Figure 1. Proposed workflow.
Figure 2
Figure 2. Distribution analysis of numerical features in CKD dataset.
Figure 3
Figure 3. Distribution analysis of categorical features in CKD dataset.
Figure 4
Figure 4. Correlation analysis of features in CKD dataset.
Figure 5
Figure 5. Performance comparison with ML/DL methods without feature selection.
Figure 6
Figure 6. Proposed model performance.
Figure 7
Figure 7. Performance comparison of existing and proposed methods.
See this image and copyright information in PMC

Similar articles

See all similar articles

References

    1. Belur Nagaraj S, Pena MJ, Ju W, Heerspink HL, BEAt-DKD Consortium Machine-learning-based early prediction of end-stage renal disease in patients with diabetic kidney disease using clinical trials data. Diabetes, Obesity and Metabolism. 2020;22(12):2479–2486. doi: 10.1111/dom.14178. - DOI - PMC - PubMed
    1. Daniel AJ, Buchanan CE, Allcock T, Scerri D, Cox EF, Prestwich BL, Francis ST. Automated renal segmentation in healthy and chronic kidney disease subjects using a convolutional neural network. Magnetic Resonance in Medicine. 2021;86(2):1125–1136. doi: 10.1002/mrm.28768. - DOI - PubMed
    1. De Almeida KL, Lessa L, Peixoto AB, Gomes RL, Celestino J. Kidney failure detection using machine learning techniques. 8th International Workshop on Advances in ICT Infrastructures and Services (ADVANCE 2020); January 27th–29th 2020; Cancún, Mexico. 2020. pp. 1–8.
    1. Deepika B, Rao V, Rampure DN, Prajwal P, Gowda D. Early prediction of chronic kidney disease by using machine learning techniques. American Journal of Computer Science and Engineering Survey. 2020;8(2):7.
    1. Dovgan E, Gradišek A, Luštrek M, Uddin M, Nursetyo AA, Annavarajula SK, Li Y-C, Syed-Abdul S. Using machine learning models to predict the initiation of renal replacement therapy among chronic kidney disease patients. PLOS ONE. 2020;15(6):e0233976. doi: 10.1371/journal.pone.0233976. - DOI - PMC - PubMed

LinkOut - more resources