AI-Based Preventive Healthcare Using Quantum Computing

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2025 | Volume : 15 | Issue : 02 | Page :
    By

    Hamithra Jothi.D,

  • Mohamed Yousuf Ali.S,

  • Yokesh Kumar.G.A,

  1. Assistant Professor, Sri Ramakrishna Engineering College Coimbatore, Tamil Nadu, India
  2. Student, Sri Ramakrishna Engineering College Coimbatore, Tamil Nadu, India
  3. , Sri Ramakrishna Engineering College Coimbatore, Tamil Nadu, India

Abstract

With its improved performance and capabilities, quantum machine learning (QML) is becoming a promising field, especially in the healthcare industry for tasks like early heart disease prediction. In this work, a Quantum Support Vector Classifier (QSVC) is proposed as the basic classifier for a bagging ensemble learning model. Shapley Additive explanations (SHAP) are used to evaluate the significance of each attribute in the predictions in order to improve explainability. Using the Cleveland dataset, an experimental study contrasts classical models like Support Vector Machines (SVM) and Artificial Neural Networks (ANN) with quantum classifiers like QSVC, Quantum Neural Networks, and Variational Quantum Classifiers. The results of our study demonstrate that the Quantum Support Vector Classifier (QSVC) surpasses all other models, achieving the highest accuracy of 90.16%. These findings underscore the potential of quantum machine learning models in comparison to traditional machine learning approaches for heart disease prediction. By leveraging quantum computing principles, QSVC exhibits superior performance, making it a promising tool for enhancing diagnostic accuracy. Furthermore, our study highlights the effectiveness of the proposed bagging ensemble method, which contributes to improved predictive capabilities. This research paves the way for integrating quantum-based models in medical diagnosis, offering a more reliable and efficient approach to identifying heart disease.

Keywords: quantum machine learning, Quantum Support Vector Classifier,QML, CVDs, QSVC

[This article belongs to Journal of Nanoscience, NanoEngineering & Applications ]

How to cite this article:
Hamithra Jothi.D, Mohamed Yousuf Ali.S, Yokesh Kumar.G.A. AI-Based Preventive Healthcare Using Quantum Computing. Journal of Nanoscience, NanoEngineering & Applications. 2025; 15(02):-.
How to cite this URL:
Hamithra Jothi.D, Mohamed Yousuf Ali.S, Yokesh Kumar.G.A. AI-Based Preventive Healthcare Using Quantum Computing. Journal of Nanoscience, NanoEngineering & Applications. 2025; 15(02):-. Available from: https://journals.stmjournals.com/jonsnea/article=2025/view=211447


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Regular Issue Subscription Original Research
Volume 15
Issue 02
Received 25/03/2025
Accepted 29/03/2025
Published 26/05/2025
Publication Time 62 Days
216

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