Modelling of biomedical data using Quantum Computing approach

Resumen

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. 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. In the first case study of the diabetes dataset, we used two different approaches. In the first approach, we presented a Quantum versus classical implementation of Machine learning (ML) algorithm applied to a diabetes dataset. Diabetes is a Sixth deadliest disease in the world and approximately 10 million new cases are registered every year worldwide. The proposed system tackles a binary classification problem of patients with diabetes into two different classes: diabetes patients with acute diseases and diabetes patients without acute diseases. Our study compares classical and quantum algorithms, namely Decision Tree, Random Forest, Extreme Boosting Gradient and Adaboost, Qboost, Voting Model 1, Voting Model 2, Qboost Plus, New model 1 and New Model 2 along with an ensemble method which creates a strong classifier from a committee of weak classifiers. The results we achieved using the validation metrics of the New Model 1 showed an overall precision of 69%, a recall of 69%, an F1-Score of 69%, a specificity of 69% and an accuracy of 69% on our diabetes dataset, with an increase of the computation speed by 55 times in comparison of the classical system. In the second approach, we presented the application of a Variational Quantum Classifier (VQC) for binary classification of the diabetes 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. In the second case study, we presented cardiovascular diseases (CVD) as conditions affecting the heart and blood vessels. Most approaches for the prediction of ischemic heart disease (IHD) have centered on pain symptoms, age, and gender. However, numerous variables have been identified as determining risk factors for developing IHD. This case study presents a collection of computationally efficient QML algorithms for cardiovascular illness classification, including Optimized Quantum Support Vector Machine (OQSVM) and Hybrid Quantum Multi-Layer Perceptron (HQMLP). Effective pre-processing and feature selection techniques, such as wrapper and filter, enhance prediction rate and ensure the robustness of the proposed frameworks. The proposed model performance metrics are compared to those of recently published and conventional models with complex architectures. The greatest accuracy of the proposed Support Vector Machine (SVM), OQSVM, Multilayer Perceptron (MLP), and HQMLP models are 96%, 94%, 94%, and 93%, respectively, when 10 features of the cardiovascular dataset are taken into consideration. Furthermore, the our proposed studies are computationally efficient and have the potential to be beneficial in real-time healthcare applications. This Ph.D. dissertation presents one conference and 2 journals published articles in the Q1 journals.