Diabetes Type 2: Poincar�Data Preprocessing for Quantum Machine Learning

Sierra-Sosa, Daniel; Arcila-Moreno, Juan D.; Garcia‐Zapirain, Begonya; Elmaghraby, Adel

doi:10.32604/cmc.2021.013196

Cited by 8 publications

(17 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Havlicek et al [12] need for extra error-correction approaches. The cost function is calculated using iterative device measurements, which helps to mitigate errors by integrating noisy data in the optimization computations [21,23]. This quantum method uses the mapping of classical input data to an increasingly ample quantum feature space, which is based on quantum circuits that are difficult to mimic conventionally.…”

Section: B Variational Quantum Classifiermentioning

confidence: 99%

“…We evaluated the competitiveness of diabetes outcomes to previously presented studies with advanced designs.D.Sierra-Sosa et al [23] utilized the VQC model to predict diabetes with acute disease and reached a maximum accuracy of 72% on the Diabetes dataset with three variables. Despite the use of three separate attention processes in conjunction with the VQC model, the results of this investigation were satisfactory.…”

Section: Diabetes Datasetmentioning

confidence: 99%

“…Despite the fact that the DWave system was utilized to merge two different attention processes with the QBoost and voting models in this investigation, the findings were promising. On the other hand, Author Method Accuracy D. Sierra-Sosa et al [23] VQC 72% H. Gupta et al [24] VQC 74% D. Maheshwari et al [25] Qboost + Voting Model 68.3% Proposed Amplitude Encoding + VQC 74.4% In order to compares SVM, QSVM, VQC and amplitude encoding VQC in terms of accuracy, recall, precision, and F1score. SVM has performed slightly better than QSVM, VQC, and amplitude encoding VQC in terms of overall accuracy.…”

Section: Diabetes Datasetmentioning

confidence: 99%

“…S. Chakraborty et al [22] presented a hybrid quantum feature selection algorithm (HQFSA) model that uses quantum parallel amplitude estimation and amplitude amplification to solve the binary classification problem for the UCI sonar dataset and compute the computing power, and they achieved overall 74% accuracy on HQFSA model. D.Sierra-Sosa et al [23] have developed a pre-processing pipeline technique that uses feature scaling, feature selection, ellipsoidal coordinate map, and stoke parameters to use VQC to determine whether diabetes is associated with acute illnesses. In that article, they examined the normalized and zero standard deviation, ellipsoidal transform, and Poincare sphere in the domain of VQC by using the two features and three features diabetes mellitus dataset.…”

Section: Introductionmentioning

confidence: 99%

“…The diabetes dataset is the primary focus of this study. Diabetes is the sixth most prominent cause of death worldwide, but Type 2 diabetes mellitus is a severe public health concern with a significantly growing problem for the world [23,25,27]. Around 463 million people have diabetes, 232 million have undiagnosed diabetes, and 4.2 million people died due to the symptoms that stemmed from diabetes in 2019 [25,28].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Variational Quantum Classifier for Binary Classification: Real vs Synthetic Dataset

2022

Self Cite

View full text Add to dashboard Cite

Nowadays, quantum-enhanced methods have been widely studied to solve machine learning related problems. This article presents the application of a Variational Quantum Classifier (VQC) for binary classification. We utilized three datasets: a synthetic dataset with randomly generated values between 0 and 1, the publicly available UCI sonar dataset consisting of mining data, and a proprietary diabetes dataset related to diabetes with acute diseases and diabetes without acute disease. To deal with the limitation of noisy intermediate-scale quantum systems (NISQ), we used a pre-processing method to enhance the prediction rate when applying the VQC method. The process includes feature selection and state preparation. Quantum state preparation is critical for obtaining a functioning pipeline in a quantum machine learning (QML) model. Amplitude encoding is a state preparation approach that enhances the performance of data encoding and the learning of quantum models. As a result, our proposed methods achieved accuracies of 75%, 71.4%, and 68.73% by using VQC model and in contrast, the amplitude encoding-based VQC achieved 98.40%, 67.3%, and 74.50% accuracies on the synthetic, sonar, and diabetes dataset, respectively.INDEX TERMS Quantum machine learning, state preparation, amplitude encoding, variational quantum classifier and T2DM diabetes.

show abstract

Section: B Variational Quantum Classifiermentioning

confidence: 99%

Section: Diabetes Datasetmentioning

confidence: 99%

Section: Diabetes Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Variational Quantum Classifier for Binary Classification: Real vs Synthetic Dataset

2022

Self Cite

View full text Add to dashboard Cite

show abstract

Quantum Machine Learning Applications in the Biomedical Domain: A Systematic Review

2022

Self Cite

View full text Add to dashboard Cite

Quantum technologies have become powerful tools for a wide range of application disciplines, which tend to range from chemistry to agriculture, natural language processing, and healthcare due to exponentially growing computational power and advancement in machine learning algorithms. Furthermore, the processing of classical data and machine learning algorithms in the quantum domain has given rise to an emerging field like quantum machine learning. Recently, quantum machine learning, become a quite challenging field in case of healthcare applications. As a result, quantum machine learning has become a common and effective technique for data processing and classification across a wide range of domains. Consequently, quantum machine learning is the most commonly used application of quantum computing. The main objective of this work is to present a brief overview of current state-of-the-art published articles between 2013 and 2021 to identify, analyze, and classify the different QML algorithms and applications in the biomedical field. Furthermore, the approach adheres to the requirements for conducting systematic literature review techniques such as research questions and quality metrics of the articles. Initially, we discovered 3149 articles, excluded the 2847 papers, and read the 121 full papers. Therefore, this research compiled 30 articles that comply with the quantum machine learning models, and quantum circuits are using biomedical data. Eventually, this article provides a broad overview of quantum machine learning limitations and future prospects.

show abstract

A Fully Connected Quantum Convolutional Neural Network for Classifying Ischemic Cardiopathy

et al. 2022

Self Cite

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

Diabetes Type 2: Poincar�Data Preprocessing for Quantum Machine Learning

Cited by 8 publications

References 31 publications

Variational Quantum Classifier for Binary Classification: Real vs Synthetic Dataset

Variational Quantum Classifier for Binary Classification: Real vs Synthetic Dataset

Quantum Machine Learning Applications in the Biomedical Domain: A Systematic Review

A Fully Connected Quantum Convolutional Neural Network for Classifying Ischemic Cardiopathy

Contact Info

Product

Resources

About