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International Conference on IoT Based Control Networks and Intelligent Systems (ICICNIS)

Parkinson's Disease Detection Using Q-LSTM

Arepalli Peda Gopi, K. Maneesha, Marni Srija, T. Sk. SadifBanu, T. Likhitha, Ramakrishna Tumati

Published Dec 17, 2024• 7 citations
Publisher

Abstract

A novel technique in early Parkinson's disease diagnosis with the use of a quantum long short-term memory model is described in this paper. Quantum computing, therefore, coupled with deep learning, seeks to improve the accuracy as well as efficiency of classifying traditional LSTM models, which even becomes more critical in cases of neurodegenerative diseases like Parkinson's disease, where diagnostics at an early stage would be helpful. The model was tested heavily on public datasets and compared with traditional deep learning models, which were significantly outperformed by this model with its superior accuracy and precision and recall rates. Furthermore, the computation time hugely became lower and thus possibly may revolutionize diagnostic procedures. These innovations will eventually establish quantum machine learning in medicine at a more accurate and faster diagnosis pace. Future development will focus on expanding the quantum LSTM so it can be more generally applicable to various situations for other medical conditions. Researchers believe that the configuration of the quantum layers should be modified, and this model will be extended to other deep learning models to have a more comprehensive and versatile tool in diagnosis for Parkinson's disease and perhaps other medical uses. The ultimate vision in the long term would be to use quantum-based models, not only for early detection but also improvement in prediction and personalization of treatment to revolutionize health care and thereby improve patient outcomes with neurodegenerative diseases.

DOI
10.1109/icicnis64247.2024.10823156

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A novel technique in early Parkinson's disease diagnosis with the use of a quantum long short-term memory model is described in this paper. Quantum computing, therefore, coupled with deep learning, seeks to improve the accuracy as well as efficiency of classifying traditional LSTM models, which even becomes more critical in cases of neurodegenerative diseases like Parkinson's disease, where diagnostics at an early stage would be helpful. The model was tested heavily on public datasets and compared with traditional deep learning models, which were significantly outperformed by this model with its superior accuracy and precision and recall rates. Furthermore, the computation time hugely became lower and thus possibly may revolutionize diagnostic procedures. These innovations will eventually establish quantum machine learning in medicine at a more accurate and faster diagnosis pace. Future development will focus on expanding the quantum LSTM so it can be more generally applicable to various situations for other medical conditions. Researchers believe that the configuration of the quantum layers should be modified, and this model will be extended to other deep learning models to have a more comprehensive and versatile tool in diagnosis for Parkinson's disease and perhaps other medical uses. The ultimate vision in the long term would be to use quantum-based models, not only for early detection but also improvement in prediction and personalization of treatment to revolutionize health care and thereby improve patient outcomes with neurodegenerative diseases.
Parkinson's disease (PD) is a progressive neuro-degenerative condition with an urgent need for timely and pinpoint diagnosis for adequate treatment. Despite potential for prediction with traditional machine learning techniques, PD data remains difficult to solve for traditional machine learning techniques due to the high computational complexity and the nature of data. We introduce in this work a Hybrid Quantum-Classical AI Framework that combines advantages of QC and provide benefits in predictive accuracy as well as computational efficiency for real time Parkinson's disease detection. We use classical deep learning models in conjunction with Variational Quantum Circuits (VQC) to perform efficient feature extraction and classification of biomedical signals: speech and its recordings, gait analysis, and EEG data. We evaluate the performance of our proposed framework against deep learning models that are standard and based on the accuracy, precision, recall and processing time. Our experimental results suggest that our hybrid quantum classical model outperforms classical methods for both accuracy and efficiency, demonstrating the potential of quantum machine learning (QML) in healthcare. This work gives a taste of the quantum revolution realized in medical diagnostics and paves the way for what could in the future be quantum enhanced disease prediction.
There is presently no cure for Parkinson's disease (PD), a degenerative and devastating neurological ailment that affects millions of people worldwide. The field of PD could benefit significantly from quantum computing, an emerging technology that has the potential to revolutionize healthcare research. After introducing Parkinson's disease (PD), its symptoms, and current treatments, this paper dives into the role that quantum computing could play in PD diagnosis and treatment. The research explains how quantum machine learning algorithms might enhance the accuracy of PD diagnosis and prediction by analyzing complicated datasets, including genetic data and medical pictures, to uncover biomarkers for early identification. In addition, the feasibility of using quantum computing to expedite drug discovery and back up personalized medicine approaches in PD treatment is investigated. The constraints and ethical concerns of applying quantum computing to healthcare are also discussed. Research is needed to address the limits of quantum computing technology. Still, the study does imply that quantum computing has the potential to dramatically improve healthcare applications, particularly in the field of neurological illnesses.
© 2026, Dr. Yashwant Research Labs Pvt. Ltd. All rights reserved.The presented investigation opens up a novel avenue that fuses quantum-assisted deep learning algorithms for the detection at the earliest possible stage of neurodegenerative diseases. The system that unites the pattern recognition capabilities of deep learning and the quantum computing power is aimed at greatly improving the diagnostic accuracy for the initial stages. This method highlights the exploitation of hybrid quantum-classical architectures to execute the complicated and multidimensional medical dataset that captures subtle interrelationship and disturbances that may not be revealed by the traditional models. The synthesis of the quantum feature encoding with the neural architectures that become more sophisticated provides for quicker convergence and better generalization over the various patient sets of data. Also, the model contains a changeable learning method that gets the current clinical patterns and, consequently, be able to timely and more accurately. The innovative architecture not only improves the prediction results but also facilitates the development of more scalable and energy-efficient solutions in the area of neuroinformatics...
As a degenerative disease that seriously affects the central nervous system, early diagnosis of Parkinson's disease is important for slowing down the course of the disease and improving patients' quality of life.In recent years, machine learning has shown great potential in medical data analysis, and support vector machines (SVMs) are widely used in classification and prediction tasks.However, traditional SVMs have some limitations in high dimensionality and complex data processing.With the rapid development of quantum computing, quantum support vector machines (QSVMs) have become an emerging technology that has attracted much attention due to its ability to process largescale data and its potential for modeling high-dimensional features.In this paper, we compare traditional SVMs and QSVMs in the analysis of Parkinson's disease data, and evaluate their performance in terms of classification accuracy.The experiments are conducted on a publicly available dataset and analyzed in detail through the steps of model construction, data preprocessing, algorithm implementation, and performance evaluation.The results show that QSVM outperforms traditional SVM in complex feature space, which demonstrates its potential advantages and application prospects in medical data analysis.
Neurodegenerative diseases are a group of progressive disorders that affect the nervous system, characterized by a gradual deterioration in mental and physical functions.In the domain of neurodegenerative diseases, Alzheimer's disease (AD) and Parkinson's disease (PD) are some of the most prevalent diseases that are very difficult to diagnose in advance stages.In order to address the issue of the present methods that are currently practiced for the diagnosis of neurodegenerative diseases, a novel diagnostic approach, termed as Neuro-Q-Net, is proposed in the developmental work of this study that possesses the capability to diagnose more than one neurodegenerative disease at a time using the Fluorodeoxyglucose Positron Emits Scan images taken from the brain of a human.In order to address the issue of unavailability of imbalanced scans in the domain of medicine, the utilization of a novel approach in computer science, termed as a Generational Adversarial Network (Gan), is suggested that assist in generating highquality images to increase the efficiency of neural networks.We use deep convolutional neural networks for feature identification, followed by Quantum Machine Learning.Our experiments show that this approach outperforms existing models in accuracy, specificity, and sensitivity.The proposed system offers an effective clinical tool for diagnosing neuro-degenerative illnesses.
‘Quantum Convolutional Neural Networks’ - QCNN is quantum machine learning model that has the potential of improving the accuracy of Alzheimer’s Disease (AD) diagnosis. Alzheimer’s disease - a neurodegenerative disorder - is considered to be the major cause of Dementia. Early diagnosis of AD is critical for improving patient outcomes. However, current diagnostic methods are often less accurate and time-consuming. QCNNs are able to learn complex features from data, which could be used to identify early signs of AD thereby resulting in an early diagnosis of a disease. This proposed technique will investigate the use of QCNN for AD diagnosis. A QCNN will be trained on a large image dataset from healthy controls & AD patients. The QCNN will then be fine-tuned on a smaller image datasets of patients with suspected AD. The performance of the QCNN will be evaluated on a test dataset of images from patients with suspected AD. This work attempts to design and develop a technique for early disease diagnosis. The QCNN could achieve 88% accuracy in classifying the data(2022) Li et al.[2]. They used a QCNN to diagnose AD from brain functional connectivity data. In another study, by Chen et al.[3], a QCNN could achieve 90% accuracy in classifying the images (2022). So taking inspiration from this, we are proposing to fine tune the QCNN in such a way as to further improve its performance and thereby resulting in more accuracy. The results of the proposed work would highlight the potential of QCNN for AD early diagnosis. If the results are promising, QCNN could be a valuable tool for early diagnosis of Alzheimer’s disease and thereby assists in saving millions of lives.