Research Article Comparing the Prognostic Value of Stress Myocardial Perfusion Imaging by Conventional and Cadmium-Zinc Telluride Single- Photon Emission Computed Tomography through a Machine Learning Approach Valeria Cantoni , 1 Roberta Green , 1 Carlo Ricciardi , 2,3 Roberta Assante, 1 Leandro Donisi, 1 Emilia Zampella, 1 Giuseppe Cesarelli, 3,4 Carmela Nappi, 1 Vincenzo Sannino, 2 Valeria Gaudieri, 1 Teresa Mannarino, 1 Andrea Genova, 1 Giovanni De Simini, 1 Alessia Giordano, 1 Adriana D’Antonio, 1 Wanda Acampa, 1,5 Mario Petretta, 6 and Alberto Cuocolo 1 1 Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy 2 Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, Italy 3 Bioengineering Unit, Institute of Care and Scientific Research Maugeri, Telese Terme, Campania, Italy 4 Department of Chemical, Materials and Production Engineering, University of Naples Federico II, Naples, Italy 5 Institute of Biostructure and Bioimaging, National Council of Research, Naples, Italy 6 IRCCS SDN, Naples, Italy Correspondence should be addressed to Carlo Ricciardi; carloricciardi.93@gmail.com Received 8 June 2021; Revised 30 September 2021; Accepted 5 October 2021; Published 16 October 2021 Academic Editor: Rafik Karaman Copyright © 2021 Valeria Cantoni et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. We compared the prognostic value of myocardial perfusion imaging (MPI) by conventional- (C-) single-photon emission computed tomography (SPECT) and cadmium-zinc-telluride- (CZT-) SPECT in a cohort of patients with suspected or known coronary artery disease (CAD) using machine learning (ML) algorithms. A total of 453 consecutive patients underwent stress MPI by both C-SPECT and CZT-SPECT. The outcome was a composite end point of all-cause death, cardiac death, nonfatal myocardial infarction, or coronary revascularization procedures whichever occurred first. ML analysis performed through the implementation of random forest (RF) and k-nearest neighbors (KNN) algorithms proved that CZT-SPECT has greater accuracy than C-SPECT in detecting CAD. For both algorithms, the sensitivity of CZT-SPECT (96% for RF and 60% for KNN) was greater than that of C-SPECT (88% for RF and 53% for KNN). A preliminary univariate analysis was performed through Mann-Whitney tests separately on the features of each camera in order to understand which ones could distinguish patients who will experience an adverse event from those who will not. Then, a machine learning analysis was performed by using Matlab (v. 2019b). Tree, KNN, support vector machine (SVM), Naïve Bayes, and RF were implemented twice: first, the analysis was performed on the as-is dataset; then, since the dataset was imbalanced (patients experiencing an adverse event were lower than the others), the analysis was performed again after balancing the classes through the Synthetic Minority Oversampling Technique. According to KNN and SVM with and without balancing the classes, the accuracy (p value = 0.02 and p value = 0.01) and recall (p value = 0.001 and p value = 0.03) of the CZT-SPECT were greater than those obtained by C-SPECT in a statistically significant way. ML approach showed that although the prognostic value of stress MPI by C-SPECT and CZT-SPECT is comparable, CZT-SPECT seems to have higher accuracy and recall. Hindawi Computational and Mathematical Methods in Medicine Volume 2021, Article ID 5288844, 8 pages https://doi.org/10.1155/2021/5288844