Heart Failure Detection Using Quantum-Enhanced Machine Learning and Traditional Machine Learning Techniques for Internet of Artificially Intelligent Medical Things

Table 1

QML-based different research fields.

Authors	QML algorithm	Research area	Description
Dang et al. [25]	Quantum KNN	Parallel computing	The author used quantum -nearest-neighbour algorithm to achieve better efficiency in image classification with 83.1% classification accuracy on the Graz-01 dataset and 78% on Caltech-101 dataset.
Lu et al. [26]	Quantum-decision tree	Pattern computation	The author proposed the quantum decision tree model that implemented Neumann entropy in place of Shannon entropy to decide which attribute should be split effectively.
Bang et al. [7]	Quantum -mean clustering	Diabetes prediction	The authors computed the global optima of the parameters by the enhanced quantum-inspired evolutionary fuzzy -means (EQIE-FCM) algorithm.
Bharill et al. [27]	Quantum -mean and quantum fuzzy -means	Image segmentation	The author proposed four quantum-based clustering algorithms to explore and evaluate the purpose of image segmentation.
Wang et al. [28]	Quantum genetic algorithm	Function optimization	The author proposed a quantum genetic algorithm which is better than the conventional genetic algorithm for computational speed.
Cong et al. [4]	Quantum-CNN	Quantum information theory	The author used quantum-CNN (QCNN) architecture to intertwine the multi-scale entanglement renormalization approach and quantum error correction.
Chen et al. [23]	Quantum-inspired forest	Feature ensembles	The author assigned each principal component a fraction-transition probability where they incorporated the QIS method into random forest and proposed quantum-inspired forest.
Taha et al. [29]	Quantum recurrent neural network	Electro encephalography signals	The author described auto-regressive (AR) model and quantum recurrent neural network (QRNN) and their proposed method, achieving an accuracy of 88.28%.
Wallach et al. [30]	Quantum-neural network	Big data	QNN inherits the basic properties of ANN and contains quantum computing paradigms. It has its application in automated control systems (ACS) and other associative memory devices.
Sosa et al. [31]	Variational quantum classification	Dementia prediction	The authors built a form of variational quantum classification to enable dementia prediction in older people.
Amin et al. [32]	Quantum neural network	COVID-19	For the analysis of COVID-19 pictures, the authors studied quantum machine learning and conventional machine learning methodologies.
			They achieved 0.94 precision, accuracy, recall, and -score on POF hospital dataset while 0.96 precision, 0.96 accuracy, 0.95 recall, and 0.96 -score on UCSD-AI4H dataset.
Gupta et al. [33]	Quantum inspired binary classifier	Diabetes prediction	The authors built a prognostic tool based on the PIMA Indian diabetes dataset to assist physicians in lowering diabetes-related mortality.
Tiwari et al. [34]	Quantum dot synthesis	Healthcare	The authors investigated the basics, synthesis, and applications of quantum dots, emphasizing the healthcare sector.
Martin et al. [35]	Quantum neural network	Healthcare	The authors presented a framework for hybrid quantum machine learning-based health status diagnostics and prognostics.