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

TLDR

Quantum‑enhanced machine learning is increasingly applied in healthcare for chronic disease detection, patient data management, and clinical trials, offering robust analytical capabilities. The study reviews recent advances in quantum‑enhanced machine learning for heart‑failure detection and quantifies differences between quantum and traditional algorithms to guide optimal method selection. The authors normalized the 14‑attribute heart‑failure dataset using min‑max scaling, PCA, and standard scaling, then optimized the qubit representation via a pipelining technique. Quantum algorithms, particularly quantum random forest, achieved superior performance, attaining 0.89 accuracy, 0.88 F1, 0.93 recall, 0.89 precision, and a 150‑microsecond faster execution time compared to traditional methods.

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.