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Abstract

Parkinson's disease (PD) is a progressive neurological disorder that primarily affects individuals over the age of 55. It is characterized by a range of motor and non-motor symptoms that can significantly impact various aspects of daily life. Despite notable advancements in medical science, there is currently no permanent cure or definitive treatment for PD. This therapeutic gap underscores the critical importance of early diagnosis, which remains a major focus of ongoing research. Due to the disease's gradual progression, PD symptoms may take years to fully develop, making early detection essential for improving patient outcomes and quality of life. Moreover, the clinical presentation of PD overlaps with several other neurological conditions, highlighting the need for accurate and reliable diagnostic methods. This study proposes a novel framework for analyzing auditory data to enhance the precision of Parkinson's disease diagnosis. By integrating quantum computing with neural network architectures, the proposed approach aims to improve diagnostic accuracy and support early detection and intervention. The quantum component facilitates more precise and efficient computations, while the neural model enhances information flow and feature extraction. Specifically, a Quantum Convolutional Neural Network (QCNN) is developed and evaluated using auditory data for PD diagnosis. The QCNN demonstrated exceptional performance on Dataset C, which includes 1,000 samples, achieving an accuracy of 99%, a precision of 99.5%, a recall of 99.5%, and an F1-score of 100%. Compared to classical convolutional neural networks, the proposed QCNN offers several advantages. Quantum algorithms are capable of performing specific computations significantly faster than their classical counterparts, leading to reduced processing time. Furthermore, they allow for a more compact representation of features, which lowers model complexity while enhancing overall performance and diagnostic accuracy.

Reason for Retraction

This article has been retracted at the request of the Editorial Office, following an internal investigation conducted in accordance with the Committee on Publication Ethics (COPE) Retraction Guidelines.

The investigation identified serious concerns affecting the integrity and reliability of the published work. Specifically, one or more of the following issues were confirmed:

  1. Undisclosed use of computer-generated text and/or data, in which substantial portions of the content were produced using algorithmic or artificial intelligence–based tools without transparent disclosure, contrary to the journal's authorship and transparency policies.

  2. Compromised peer-review process, indicating irregularities that undermine the validity, independence or authenticity of the review procedure.

  3. Inappropriate or misleading citations, including references that are irrelevant, improperly used, or appear to artificially inflate citation metrics, thereby distorting the scholarly record.

  4. Authorship-related concerns, including the addition of new author(s) at a later stage of the publication process without adequate justification, documentation, or transparent disclosure, raising unresolved questions regarding author contributions, responsibility, and compliance with the journal's authorship criteria.

The Editorial Office determined that these issues significantly compromise the scientific integrity of the article, and that correction alone would be insufficient to address the concerns. Retraction was therefore deemed necessary to maintain the accuracy and trustworthiness of the scholarly record.

The authors were informed of the findings and the retraction decision. While the authors do not respond to this retraction, the journal has proceeded with the retraction in line with COPE guidance, which permits retraction without author consent when editorial integrity is at risk.

This retraction is issued to alert readers that the findings and conclusions of the article should not be relied upon. The original article will remain accessible for the sake of the scholarly record, but it will be clearly marked as retracted.

Apologies are offered to readers of the journal that this was not detected during the submission process.

Please see the Retraction Notice available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss3/13.

DOI: https://doi.org/10.52866/2788-7421.1350

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