BackgroundFinding a valid diagnosis is mostly a prolonged process. Current advances in the sector of artificial intelligence have led to the appearance of expert systems that enrich the experiences and capabilities of doctors for making decisions for their patients.ObjectiveThe objective of this research was developing a fuzzy expert system for diagnosing Cystic Fibrosis (CF).MethodsDefining the risk factors and then, designing the fuzzy expert system for diagnosis of CF were carried out in this cross-sectional study. To evaluate the performance of the proposed system, a dataset that corresponded to 70 patients with respiratory disease who were serially admitted to the CF Clinic in the Pediatric Respiratory Diseases Center, Masih Daneshvari Hospital in Tehran, Iran during August 2016 to January 2017 was considered. Whole procedures of system construction were implemented in a MATLAB environment.ResultsResults showed that the suggested system can be used as a strong diagnostic tool with 93.02% precision, 89.29% specificity, 95.24% sensitivity and 92.86% accuracy for diagnosing CF. There was also a good relationship between the user and the system through the appealing user interface.ConclusionThe system is equipped with information, knowledge, and expertise from certified specialists; hence, as a training tool it can be useful for new physicians. It is worth mentioning that the accomplishment of this project depends on advocacy of decision making in CF diagnosis. Nevertheless, it is expected that the system will reduce the number of false positives and false negatives in unusual cases.
Background Predicting severe respiratory failure due to COVID-19 can help triage patients to higher levels of care, resource allocation and decrease morbidity and mortality. The need for this research derives from the increasing demand for innovative technologies to overcome complex data analysis and decision-making tasks in critical care units. Hence the aim of our paper is to present a new algorithm for selecting the best features from the dataset and developing Machine Learning(ML) based models to predict the intubation risk of hospitalized COVID-19 patients. Methods In this retrospective single-center study, the data of 1225 COVID-19 patients from February 9, 2020, to July 20, 2021, were analyzed by several ML algorithms which included, Decision Tree(DT), Support Vector Machine (SVM), Multilayer perceptron (MLP), and K-Nearest Neighbors(K-NN). First, the most important predictors were identified using the Horse herd Optimization Algorithm (HOA). Then, by comparing the ML algorithms' performance using some evaluation criteria, the best performing one was identified. Results Predictive models were trained using 12 validated features. Also, it found that proposed DT-based predictive model enables a reasonable level of accuracy (=93%) in predicting the risk of intubation among hospitalized COVID-19 patients. Conclusions The experimental results demonstrate the effectiveness of the proposed meta-heuristic feature selection technique in combining with DT model in predicting intubation risk for hospitalized patients with COVID-19. The proposed model have the potential to inform frontline clinicians with quantitative and non-invasive tool to assess illness severity and identifying high risk patients .
Introduction: The COVID-19 epidemic is currently fronting the worldwide health care systems with many qualms and unexpected challenges in medical decision-making and the effective sharing of medical resources. Machine Learning (ML)-based prediction models can be potentially advantageous to overcome these uncertainties. Objective: This study aims to train several ML algorithms to predict the COVID-19 in-hospital mortality and compare their performance to choose the best performing algorithm. Finally, the contributing factors scored using some feature selection methods. Material and Methods: Using a single-center registry, we studied the records of 1353 confirmed COVID-19 hospitalized patients from Ayatollah Taleghani hospital, Abadan city, Iran. We applied six feature scoring techniques and nine well-known ML algorithms. To evaluate the models’ performances, the metrics derived from the confusion matrix calculated. Results: The study participants were 1353 patients, the male sex found to be higher than the women (742 vs. 611), and the median age was 57.25 (interquartile 18-100). After feature scoring, out of 54 variables, absolute neutrophil/lymphocyte count and loss of taste and smell were found the top three predictors. On the other hand, platelet count, magnesium, and headache gained the lowest importance for predicting the COVID-19 mortality. Experimental results indicated that the Bayesian network algorithm with an accuracy of 89.31% and a sensitivity of 64.2 % has been more successful in predicting mortality. Conclusion: ML provides a reasonable level of accuracy in predicting. So, using the ML-based prediction models facilitate more responsive health systems and would be beneficial for timely identification of vulnerable patients to inform appropriate judgment by the health care providers. Abbreviation: Coronavirus Disease 2019 (COVID‐19), World Health Organization (WHO), Machine Learning (ML), Artificial Intelligence (AI), Multilayer Perceptron (MLP), Support Vector Machine (SVM), Locally Weighted Learning (LWL), Clinical Decision Support System (CDSS)
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.