Carlo Ricciardi1, Valeria Cantoni1, Giovanni Improta2, Luigi Iuppariello3, Imma Latessa2, Mario Cesarelli4, Maria Triassi2, Alberto Cuocolo1. 1. Department of Advanced Biomedical Sciences, University Hospital of Naples 'Federico II', Naples, Italy. 2. Department of Public Health, University Hospital of Naples 'Federico II', Naples, Italy. 3. Department of Neuroscience, Santobono-Pausilipon Children's Hospital, Naples, Italy. 4. DIETI, University of Naples 'Federico II', Naples, Italy; Istituti Clinici Scientifici Maugeri IRCCS; Telese Terme (BN), Italy. Electronic address: cesarell@unina.it.
Abstract
INTRODUCTION: Coronary artery disease (CAD) is still one of the primary causes of death in the developed countries. Stress single-photon emission computed tomography is used to evaluate myocardial perfusion and ventricular function in patients with suspected or known CAD. This study sought to test data mining and machine learning tools and to compare some supervised learning algorithms in a large cohort of Italian subjects with suspected or known CAD who underwent stress myocardial perfusion imaging. METHODS: The dataset consisted of 10,265 patients with suspected or known CAD. The analysis was conducted using Knime analytics platform in order to implement Random Forests, C4.5, Gradient boosted tree, Naïve Bayes, and K nearest neighbor (KNN) after a procedure of features filtering. K-fold cross-validation was employed. RESULTS: Accuracy, error, precision, recall, and specificity were computed through the above-mentioned algorithms. Random Forests and gradients boosted trees obtained the highest accuracy (>95%), while it was comprised between 83% and 88%. The highest value for sensitivity and specificity was obtained by C4.5 (99.3%) and by Gradient boosted tree (96.9%). Naïve Bayes had the lowest precision (70.9%) and specificity (72.0%), KNN the lowest recall and sensitivity (79.2%). CONCLUSIONS: The high scores obtained by the implementation of the algorithms suggests health facilities consider the idea of including services of advanced data analysis to help clinicians in decision-making. Similar applications of this kind of study in other contexts could support this idea.
INTRODUCTION:Coronary artery disease (CAD) is still one of the primary causes of death in the developed countries. Stress single-photon emission computed tomography is used to evaluate myocardial perfusion and ventricular function in patients with suspected or known CAD. This study sought to test data mining and machine learning tools and to compare some supervised learning algorithms in a large cohort of Italian subjects with suspected or known CAD who underwent stress myocardial perfusion imaging. METHODS: The dataset consisted of 10,265 patients with suspected or known CAD. The analysis was conducted using Knime analytics platform in order to implement Random Forests, C4.5, Gradient boosted tree, Naïve Bayes, and K nearest neighbor (KNN) after a procedure of features filtering. K-fold cross-validation was employed. RESULTS: Accuracy, error, precision, recall, and specificity were computed through the above-mentioned algorithms. Random Forests and gradients boosted trees obtained the highest accuracy (>95%), while it was comprised between 83% and 88%. The highest value for sensitivity and specificity was obtained by C4.5 (99.3%) and by Gradient boosted tree (96.9%). Naïve Bayes had the lowest precision (70.9%) and specificity (72.0%), KNN the lowest recall and sensitivity (79.2%). CONCLUSIONS: The high scores obtained by the implementation of the algorithms suggests health facilities consider the idea of including services of advanced data analysis to help clinicians in decision-making. Similar applications of this kind of study in other contexts could support this idea.
Authors: Alfonso Maria Ponsiglione; Carlo Ricciardi; Arianna Scala; Antonella Fiorillo; Alfonso Sorrentino; Maria Triassi; Giovanni Dell'Aversana Orabona; Giovanni Improta Journal: J Healthc Eng Date: 2021-08-17 Impact factor: 2.682
Authors: Maikel Luis Kolling; Leonardo B Furstenau; Michele Kremer Sott; Bruna Rabaioli; Pedro Henrique Ulmi; Nicola Luigi Bragazzi; Leonel Pablo Carvalho Tedesco Journal: Int J Environ Res Public Health Date: 2021-03-17 Impact factor: 3.390
Authors: Valeria Cantoni; Roberta Green; Carlo Ricciardi; Roberta Assante; Leandro Donisi; Emilia Zampella; Giuseppe Cesarelli; Carmela Nappi; Vincenzo Sannino; Valeria Gaudieri; Teresa Mannarino; Andrea Genova; Giovanni De Simini; Alessia Giordano; Adriana D'Antonio; Wanda Acampa; Mario Petretta; Alberto Cuocolo Journal: Comput Math Methods Med Date: 2021-10-16 Impact factor: 2.238