首页|Findings from Prince of Songkla University Provide New Insights into Machine Learning (Electrocardiogram Analysis for Kratom Users Utilizing Deep Residual Learning Network and Machine Learning)
Findings from Prince of Songkla University Provide New Insights into Machine Learning (Electrocardiogram Analysis for Kratom Users Utilizing Deep Residual Learning Network and Machine Learning)
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Current study results on Machine Learning have been published. According to news reporting from Hat Yai, Thailand, by NewsRx journalists, research stated, “Kratom (Mitragyna speciosa Korth) is a common tropical plant found in Southeast Asia. Its leaves possess medicinal properties and are used to treat various ailments.” Financial support for this research came from Center for Addiction Studies (CADS) and the Thai Health Promotion Foundation. The news correspondents obtained a quote from the research from the Prince of Songkla University, “However, the effects of kratom extract in terms of biological domains are still concerning. Although considerable studies have been conducted on the effects of kratom usage over the last few years, no study using in silico analysis of kratom users’ electrocardiogram (ECG) has been reported to date. This study aims to examine the long-term effects of kratom consumption using the ECG signals and deep learning (DL) network and machine learning techniques. Raw ECG signals were used as input for training and detecting abnormalities, and a deep residual learning network (DRLN) model was implemented to develop a feature extractor from single-lead datasets; the extracted features were used to train conventional machine learning classifiers. The confounding ECG abnormality factors, namely, age, sex, smoking, alcohol consumption, and exercise, were analyzed for association using the chi-square test. The main results of our study showed that kratom usage is not associated with ECG abnormalities. However, the ECG signal was affected more by gender than by the other factors; it exhibited the highest sensitivity and specificity (score = 0.63).”
Hat YaiThailandAsiaCyborgsEmerging TechnologiesMachine LearningPrince of Songkla University
NETL
NSTL
IEEE
