Dementia Prediction Applying Variational Quantum Classifier

Sierra-Sosa, Daniel; Arcila-Moreno, Juan; Garcia-Zapirain, Begonya; Castillo-Olea, Cristian; Elmaghraby, Adel

doi:10.48550/arxiv.2007.08653

Cited by 4 publications

(5 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The highlighted points are the instances for the support vectors [42]. One important advantage of SVM is that it allows to transform data into a higher dimensional space, where datasets are easily separable and the Kernel Trick is used to reduce computations [42].…”

Section: Support Vector Machinesmentioning

confidence: 99%

“…Variational Quantum Classfier is an algorithm that allows to obtain experimental results in NISQ devices without the need to perform additional error correction techniques [42]. This method is a hybrid approach where the parameters are optimized and updated in a classical computer, making the optimization process without increasing the coherence times needed.…”

Section: Variational Quantum Classifiersmentioning

confidence: 99%

See 1 more Smart Citation

Quantum Machine Learning and TensorNetworks as an Aid in the Clinical Diagnosis ofCoronary Artery Disease

Bopardikar,

Montoya,

Decoodt

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

Current applications of quantum machine learning are emerging, dueto the potential benefits that quantum technologies could bring in thenear future. One of the most recent developments is tensor network-based architectures, to explore the feasibility of the application of thismethod to healthcare, in this paper tensor networks are applied to theIEEE Heart Disease (COMPREHENSIVE) dataset for supervised clas-sification of coronary artery disease diagnosis. Three quantum machinelearning models were implemented in this study: 3-qubit Q-MPS, 4 qubitQ-MERA and 4-qubit Q-TTN. These were compared to six classical ma-chine learning models: Logistic Regression, Naive Bayes, Support VectorMachines (SVM), Decision Tree, Random Forest, and XGBoost; achiev-ing similar or better results than those of the best classical models. Themodels were evaluated following different strategies to modify the trainingand testing conditions, applying variations to the dataset by isolating theCleveland and Hungary datasets, which are subsets of the former. Addi-tionally, the impact of the input feature space preparation is demonstratedexperimentally, showing that there exist preferred conditions where thegeneralization of the model is maximum.

show abstract

Section: Support Vector Machinesmentioning

confidence: 99%

Section: Variational Quantum Classifiersmentioning

confidence: 99%

Quantum Machine Learning and TensorNetworks as an Aid in the Clinical Diagnosis ofCoronary Artery Disease

Bopardikar,

Montoya,

Decoodt

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…For instance, in this study [115], an attempt was made to predict dementia in elderly patients using IBM's VQC model within the Qiskit framework. The performance of this model was compared with that of a classical SVM with a linear kernel considering different numbers of features.…”

Section: Classification Performance Of Vqc Modelsmentioning

confidence: 99%

Beyond Limits: Charting New Horizons in Glioma Tumor Classification through Hybrid Quantum Computing with The Cancer Genome Atlas (TCGA) Data

Akpinar,

Oduncuoglu

2024

Preprint

View full text Add to dashboard Cite

Gliomas, which are the most common malignant primary brain tumors, present significant challenges in terms of varying survival rates, treatment modalities, and prognostic processes between patients with low-grade gliomas (LGGs) and high-grade gliomas (HGGs). Accurate classification and grading of LGGs and HGGs are crucial for appropriate treatment planning and assessment of overall prognosis. The classification and grading of gliomas have undergone evolution over time, and the inclusion of molecular markers in the classification of glioma tumors by the WHO Central Nervous System (CNS) Tumors Classification in 2016 and the incorporation of advanced molecular diagnostics in 2021 have improved glioma tumor characterization. However, the high cost and limited accessibility of molecular genetic tests, coupled with the time-consuming nature of obtaining results, can lead to delays in critical treatment decisions. To address these challenges, various classical artificial intelligence methods, such as machine learning and deep learning, have been applied to problems in this field, yielding a certain level of success. Nevertheless, the continuous expansion of medical data dimensions, the inherent noise level in the data, and the limitations of the classical vector space pose significant challenges that classical artificial intelligence methods struggle to overcome. Recent studies have demonstrated that the use of quantum computing and quantum artificial intelligence technologies in healthcare not only addresses these problems but also accelerates complex data analyses and processes large datasets more efficiently. This paper presents a novel hybrid quantum (or classical-quantum) computing model aimed at differentiating between LGGs and HGGs using data from The Cancer Genome Atlas (TCGA). To the best of our knowledge, this study is the first to investigate the classification of LGGs and HGGs using a hybrid classical-quantum framework within the TCGA dataset. In the classical part of the study, an ensemble feature selection method was used to identify the most important molecular markers and clinical features within the TCGA glioma dataset. In the quantum section, six variational quantum classifier (VQC) models with different hyperparameters are proposed. These classifiers are subsequently used to differentiate between LGGs and HGGs using the features obtained from the ensemble model. The computational results show that among the six VQC models, the VQC-1 model, which incorporates Rx and CX gates in the feature map and Ry, Rz and CY gates in the parameterized quantum circuit and utilizes the AQCD optimization method, achieves the highest classification accuracy of 0.74. This study provides a novel perspective on the classification of glioma tumors by combining classical and quantum computing methods.

show abstract

“…The use of quantum deep learning (QDL) and ML models for medical applications has gained significant attention in the past few years [24], [25]. In addition, various researchers have previously used different quantum machine learning algorithms [26], [27] by utilizing real-time healthcare datasets [28], [29]. The academic community and the medical field have both recently shown a substantial interest in supervised learning [30].…”

Section: Introductionmentioning

confidence: 99%

A Fully Connected Quantum Convolutional Neural Network for Classifying Ischemic Cardiopathy

et al. 2022

View full text Add to dashboard Cite

The prevalence of heart diseases is rising quickly throughout the world, which has an impact on both the world economy and public health. According to the recent statistical survey reports, the increasing mortality rate is due to high blood pressure, high cholesterol, the use of tobacco, obesity, and an inconsistent pulse rate. It is difficult and time-consuming to investigate the various variations of these factors and their impact on Coronary Artery Disease (CAD). Therefore, it is necessary to use modern approaches to diagnose the disease early and minimize the mortality rate. The fields of machine learning and data mining have a wide research dimension and various novel techniques that could help in the prediction of CAD in its early stages and identify their patterns and behaviors in a huge amount of data. The results of such predictions will aid the clinical staff in decision making and early diagnosis. In such a scenario, we proposed a quantum version of the Fully Convolutional Neural Network (FCQ-CNN) for Ischemic Heart Disease (IHD) classification. The proposed model evaluates the quantum circuit-based technique that was inspired by convolutional neural networks, a very successful machine learning model. This method provides O(log(n)) depth for n qubits, reducing the number of parameters and allowing for effective training and testing of real quantum devices. The model has been evaluated by considering the IHD dataset after the data has been cleaned and filtered through the Maximally Relevant Minimally Redundancy (MRMR) filter. For dimension reduction, a Support Vector Machine along with Recursive Feature Elimination (SVM-RFE) has been considered. Initially, the model is tested with 20% of the whole dataset and gets the promising results of a testing accuracy of 84.6% with a testing loss of 0.28. By taking into account the same optimal parameters, the proposed model outcomes are compared to those of the classical Optimized Convolution Neural Network (Optimized-CNN) and Fully Connected Neural Network (FCNN) models. Comparing the model's competency to that of earlier published quantum models yields improvements in accuracy of 8.6%, 12.6%, 3.5%, and 1.8% respectively.

show abstract

Dementia Prediction Applying Variational Quantum Classifier

Cited by 4 publications

References 25 publications

Quantum Machine Learning and TensorNetworks as an Aid in the Clinical Diagnosis ofCoronary Artery Disease

Quantum Machine Learning and TensorNetworks as an Aid in the Clinical Diagnosis ofCoronary Artery Disease

Beyond Limits: Charting New Horizons in Glioma Tumor Classification through Hybrid Quantum Computing with The Cancer Genome Atlas (TCGA) Data

A Fully Connected Quantum Convolutional Neural Network for Classifying Ischemic Cardiopathy

Contact Info

Product

Resources

About