Skip to main content

Advertisement

Springer Nature Link
Log in

Quantum K-means clustering method for detecting heart disease using quantum circuit approach

  • Focus
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

The development of noisy intermediate- scale quantum computers is expected to signify the potential advantages of quantum computing over classical computing. This paper focuses on quantum paradigm usage to speed up unsupervised machine learning algorithms particularly the K-means clustering method. The main approach is to build a quantum circuit that performs the distance calculation required for the clustering process. This proposed technique is a collaboration of data mining techniques with quantum computation. Initially, extracted heart disease dataset is preprocessed and classical K-means clustering performance is evaluated. Later, the quantum concept is applied to the classical approach of the clustering algorithm. The comparative analysis is performed between quantum and classical processing to check performance metrics.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

Availability of data and material

Yes, data and material for transparency will be made available.

Code Availability

N/A.

References

  • Abdel-Basset M, Gamal A, Manogaran G, Son LH, Long HV (2020) A novel group decision making model based on neutrosophic sets for heart disease diagnosis. Multimedia Tools Appl 79(15):9977–10002

    Article  Google Scholar 

  • Acampora G(2019) Quantum machine intelligence. Springer

  • Aïmeur E, Brassard G, Gambs S (2013) Quantum speed-up for unsupervised learning. Mach Learn 90(2):261–287

    Article  MathSciNet  MATH  Google Scholar 

  • Aleksandrowicz G, Alexander T, Barkoutsos P, Bello L, Ben-Haim Y, Bucher D, Cabrera-Hernández FJ, Carballo-Franquis J, Chen A, Chen C-F, et al (2019) Qiskit: An open-source framework for quantum computing. Accessed on: Mar, 16

  • Al-Yarimi FAM, Munassar NMA, Bamashmos MHM, Ali MYS (2021) Feature optimization by discrete weights for heart disease prediction using supervised learning. Soft Comput 25(3):1821–1831

    Article  Google Scholar 

  • Arthur D et al (2021) Balanced k-means clustering on an adiabatic quantum computer. Quant Inf Process 20(9):1–30

    Article  MathSciNet  MATH  Google Scholar 

  • Bakhsh AA (2021) High-performance in classification of heart disease using advanced supercomputing technique with cluster-based enhanced deep genetic algorithm. J Supercomput , pp 1–22

  • Benlamine, K., Bennani, Y., Grozavu, N., Matei, B (2020) Quantum collaborative k-means. In: 2020 International joint conference on neural networks (IJCNN), pp 1– 7, IEEE

  • Bharill N, Patel OP, Tiwari A ( 2015) An enhanced quantum-inspired evolutionary fuzzy clustering. In: 2015 IEEE symposium series on computational intelligence, pp 772– 779. IEEE

  • Bharti R, Khamparia A, Shabaz M, Dhiman G, Pande S, Singh P(2021) Prediction of heart disease using a combination of machine learning and deep learning. Comput Intell Neurosci

  • Biamonte J, Wittek P, Pancotti N, Rebentrost P, Wiebe N, Lloyd S (2017) Quantum machine learning. Nature 549(7671):195–202

    Article  Google Scholar 

  • Casaña-Eslava RV, Lisboa PJ, Ortega-Martorell S, Jarman IH, Martín-Guerrero JD (2020) Probabilistic quantum clustering. Knowl Based Syst 194:105567

    Article  Google Scholar 

  • Cong I, Duan L (2016) Quantum discriminant analysis for dimensionality reduction and classification. New J Phys 18(7):73011

    Article  Google Scholar 

  • Farouk MH (2017) On the application of quantum clustering on speech data. Int J Speech Technol 20(4):891–896

    Article  Google Scholar 

  • Gangal K(2021) Heart disease dataset uci (2021). https://www.kaggle.com/ketangangal/heart-disease-dataset-uci?select=HeartDiseaseTrain-Test.csv

  • Gao X-Y, Amin Ali A, Shaban Hassan H, Anwar EM (2021) Improving the accuracy for analyzing heart diseases prediction based on the ensemble method. Complexity

  • Gong C, Dong Z, Gani A, Qi H (2021) Quantum k-means algorithm based on trusted server in quantum cloud computing. Quant Inf Process 20(4):1–22

    Article  MathSciNet  MATH  Google Scholar 

  • Gupta H, Varshney H, Sharma TK, Pachauri N, Verma OP (2021) Comparative performance analysis of quantum machine learning with deep learning for diabetes prediction. Complex Intell Syst, pp 1–15

  • Haq AU,Li JP, Memon MH, Nazir S, Sun R (2018) A hybrid intelligent system framework for the prediction of heart disease using machine learning algorithms. Mobile Inf Syst

  • Huang Z (1998) Extensions to the k-means algorithm for clustering large data sets with categorical values. Data Min Knowl Dis 2(3):283–304

    Article  Google Scholar 

  • Kannan R, Vasanthi, V (2019) Machine learning algorithms with roc curve for predicting and diagnosing the heart disease. Soft Comput Med Bioinf, pp 63– 72. Springer

  • Katarya R, Meena SK (2021) Machine learning techniques for heart disease prediction: a comparative study and analysis. Health Technol 11(1):87–97

    Article  Google Scholar 

  • Keogh EJ, Mueen A (2017) Curse of dimensionality. Encycl Mach Learn Data Min 2017:314–315

    Google Scholar 

  • Kerenidis I, Landman J, Luongo A, Prakash A(2018) q-means: a quantum algorithm for unsupervised machine learning. arXiv preprint arXiv:1812.03584

  • Khan SU, Awan AJ, Vall-Llosera G(2019) K-means clustering on noisy intermediate scale quantum computers. arXiv preprint arXiv:1909.12183

  • Khan M, Hussain I, Jamal SS, Amin M (2019) A privacy scheme for digital images based on quantum particles. Int J Theor Phys 58(12):4293–4310

    Article  MATH  Google Scholar 

  • Kumar Y, Koul A, Sisodia PS, Shafi J, Kavita V, Gheisari M, Davoodi MB (2021) Heart failure detection using quantum-enhanced machine learning and traditional machine learning techniques for internet of artificially intelligent medical things. Wireless Commun Mobile Comput

  • Kuruvilla AM, Balaji N (2021) Heart disease prediction system using correlation based feature selection with multilayer perceptron approach. In: IOP conference series: materials science and engineering, vol 1085, p 012028 ( 2021). IOP Publishing

  • Lamata L ( 2021) Quantum reinforcement learning with quantum photonics. In: Photonics, vol 8, p 33 . Multidisciplinary Digital Publishing Institute

  • Li Q, He Y, Jiang J-p (2011) A hybrid classical-quantum clustering algorithm based on quantum walks. Quant Inf Process 10(1):13–26

    Article  MathSciNet  MATH  Google Scholar 

  • Liu, W., Chen, H., Yan, Q., Liu, Z., Xu, J., Zheng, Y(2010) A novel quantum-inspired evolutionary algorithm based on variable angle-distance rotation. In: IEEE congress on evolutionary computation, pp 1– 7 ( 2010). IEEE

  • Lloyd S (1982) Least squares quantization in pcm. IEEE Trans Inf Theory 28(2):129–137

    Article  MathSciNet  MATH  Google Scholar 

  • Lloyd S, Mohseni M, Rebentrost P (2014) Quantum principal component analysis. Nat Phys 10(9):631–633

    Article  Google Scholar 

  • Mohan S, Thirumalai C, Srivastava G (2019) Effective heart disease prediction using hybrid machine learning techniques. IEEE Access 7:81542–81554

    Article  Google Scholar 

  • Möller M, Vuik C (2017) On the impact of quantum computing technology on future developments in high-performance scientific computing. Ethics Inf Technol 19(4):253–269

    Article  Google Scholar 

  • Na S, Xumin L, Yong G( 2010)Research on k-means clustering algorithm: An improved k-means clustering algorithm. In: 2010 Third international symposium on intelligent information technology and security informatics, pp 63– 67. IEEE

  • Ramezani SB, Sommers A, Manchukonda HK, Rahimi S, Amirlatifi A ( 2020) Machine learning algorithms in quantum computing: A survey. In: 2020 International joint conference on neural networks (IJCNN), pp 1– 8. IEEE

  • Rani P, Kumar R, Ahmed NMS, Jain A(2021) A decision support system for heart disease prediction based upon machine learning. J Reliable Intell Environ, pp 1–13

  • Rubio E, Castillo O, Valdez F, Melin P, Gonzalez CI, Martinez G(2017) An extension of the fuzzy possibilistic clustering algorithm using type-2 fuzzy logic techniques. Adv Fuzzy Syst

  • Sarma A, Chatterjee R, Gili K, Yu T (2019) Quantum unsupervised and supervised learning on superconducting processors. arXiv preprint arXiv:1909.04226

  • Sergioli G, Santucci E, Didaci L, Miszczak JA, Giuntini R (2018) A quantum-inspired version of the nearest mean classifier. Soft Comput 22(3):691–705

    Article  MATH  Google Scholar 

  • Shao C, Li Y, Li H (2019) Quantum algorithm design: techniques and applications. J Syst Sci Complex 32(1):375–452

    Article  MathSciNet  MATH  Google Scholar 

  • Shende VV, Bullock SS, Markov IL (2006) Synthesis of quantum-logic circuits. IEEE Trans Comput Aided Des Integrated Circuits Syst 25(6):1000–1010

  • Shi C, Wei B, Wei S, Wang W, Liu H, Liu J (2021) A quantitative discriminant method of elbow point for the optimal number of clusters in clustering algorithm. EURASIP J Wireless Commun Netw 2021(1):1–16

    Article  Google Scholar 

  • Singh P, Bose SS (2021) A quantum-clustering optimization method for covid-19 ct scan image segmentation. Exp Syst Appl 185:115637

    Article  Google Scholar 

  • Wettschereck D, Aha DW, Mohri T (1997) A review and empirical evaluation of feature weighting methods for a class of lazy learning algorithms. Artif Intell Rev 11(1):273–314

    Article  Google Scholar 

  • Xu D, Tian Y (2015) A comprehensive survey of clustering algorithms. Ann Data Sci 2(2):165–193

    Article  MathSciNet  Google Scholar 

  • Yang M-S, Nataliani Y (2017) A feature-reduction fuzzy clustering algorithm based on feature-weighted entropy. IEEE Trans Fuzzy Syst 26(2):817–835

    Article  Google Scholar 

  • Yao Z, Peng W, Gao-yun C, Dong-Dong C, Rui D, Yan Z(2008) Quantum clustering algorithm based on exponent measuring distance. In: 2008 IEEE international symposium on knowledge acquisition and modeling workshop, pp 436–439. IEEE

  • Zhang Y, Ni Q (2020) Recent advances in quantum machine learning. Quant Eng 2(1):34

    Google Scholar 

Download references

Funding

The work presented came from the Ph.D. research undertaken at Department of Electronics and Communication Engineering, the National Institute of Engineering, Mysuru, Karnataka, India.

Author information

Author notes

  1. N. Kaulgud contributed equally to this work.

Authors and Affiliations

  1. Electronics and Communication Engineering, The National Institute of Engineering, Manandavadi Road, Mysuru, Karnataka, 570008, India

    S S Kavitha & Narasimha Kaulgud

Authors
  1. S S Kavitha
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Narasimha Kaulgud
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

All authors contributed to the study conception and design.

Corresponding author

Correspondence to S S Kavitha.

Ethics declarations

Conflict of interest

There is no conflict of interests that are directly or indirectly related to the work submitted for publication.

Additional information

Communicated by Oscar Castillo.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kavitha, S.S., Kaulgud, N. Quantum K-means clustering method for detecting heart disease using quantum circuit approach. Soft Comput 27, 13255–13268 (2023). https://doi.org/10.1007/s00500-022-07200-x

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-022-07200-x

Keywords