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

Abstract

Quantum-enhanced machine learning plays a vital role in healthcare because of its robust application concerning current research scenarios, the growth of novel medical trials, patient information and record management, procurement of chronic disease detection, and many more. Due to this reason, the healthcare industry is applying quantum computing to sustain patient-oriented attention to healthcare patrons. The present work summarized the recent research progress in quantum-enhanced machine learning and its significance in heart failure detection on a dataset of 14 attributes. In this paper, the number of qubits in terms of the features of heart failure data is normalized by using min-max, PCA, and standard scalar, and further, has been optimized using the pipelining technique. The current work verifies that quantum-enhanced machine learning algorithms such as quantum random forest (QRF), quantum K nearest neighbour (QKNN), quantum decision tree (QDT), and quantum Gaussian Naïve Bayes (QGNB) are better than traditional machine learning algorithms in heart failure detection. The best accuracy rate is (0.89), which the quantum random forest classifier attained. In addition to this, the quantum random forest classifier also incurred the best results in F 1 score, recall and, precision by (0.88), (0.93), and (0.89), respectively. The computation time taken by traditional and quantum-enhanced machine learning algorithms has also been compared where the quantum random forest has the least execution time by 150 microseconds. Hence, the work provides a way to quantify the differences between standard and quantum-enhanced machine learning algorithms to select the optimal method for detecting heart failure.

Quality Evaluation and Satisfaction Analysis of Online Learning of College Students Based on Artificial Intelligence

“…In order to further improve the computational speed of this algorithm, relevant theories of extreme learning machine are introduced into the WOA algorithm [17], so as to obtain extreme learning machine (ELM). ELM has good generalization ability, and its learning speed is thousands of times faster than mainstream models [18] (Figure 2). e network structure diagram of an extreme learning machine can be divided into the input hiding layer and output layer according to the di erent analysis functions.…”

Prediction of the Age and Gender Based on Human Face Images Based on Deep Learning Algorithm

“…Feature selection is then proceeded by the classification process which classifies the face image in terms of age and gender. Age is categorized into 8 classes as (0-2), (4-6), (8)(9)(10)(11)(12), (15)(16)(17)(18)(19)(20), (25)(26)(27)(28)(29)(30)(31)(32), (38)(39)(40)(41)(42)(43), (48)(49)(50)(51)(52)(53), and (60-100), and gender is categorized as male/female. Figure 7 shows the result of classification.…”

“…DBN learning is quicker than DNN due to the inclusion of RBM. The RBMs are stacked DBM with unguided connections across the levels [48][49][50][51][52][53].…”

Quantum Computing for Healthcare: A Review

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