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

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.