Tehran University of Medical Sciences Iranian Journal of Public Health 2251-6085 52 1 2023 01 07 175 183 Mitra Montazeri Medical Informatics Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran Mehdi Ahmadinejad Department of Anesthesiology, Shahid Bahonar Hospital, Afzalipour Faculty of Medicine, Kerman University of Medical Sciences, Kerman, Iran Mahdieh Montazeri 1. Medical Informatics Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran 2. Department of Health Information Sciences, Faculty of Management and Medical Information Sciences, Kerman University of Medical Sciences, Kerman, Iran Kambiz Bahaadinbeigy Medical Informatics Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran Mohadeseh Montazeri Department of Computer, Technical and Vocational University, Kerman, Iran Leila Ahmadian Kerman University of Medical Science 2020 10 10 2021 06 21 Background: Intensive Care Unit (ICU) has the highest mortality rate in the world. ICU has special equipment that leads to the hospital's most costly parts. The length of stay in the ICU is a special issue, and reducing this time is a practical approach. We aimed to use artificial intelligence to help early and timely diagnosis of the disease to help with health. Methods: We designed a rule-based intelligent system to predict the length of stay and the mortality rate of trauma patients in ICU. A neuro-Fuzzy and eight machine learning models were used to predict the mortality rate in trauma patients in ICU. The performances of these techniques were evaluated with accuracy, sensitivity, specificity, and area under the ROC curve. Decision-Table was used to predict the length of stay in trauma patients in ICU. For comparison, eight machine learning models were used. The method is compared based on Mean absolute error and relative absolute error (%). Results: Neuro-Fuzzy expert system and Decision-Table showed better results than other techniques. Accuracy, sensitivity, specificity, and ROC Area of Nero-Fuzzy are 83.6735, 0.9744, 0.3000, 0.8379, and 1, respectively. The mean absolute error and Relative absolute error (%) of the Decision-Table model are 4.5426 and 65.4391, respectively. Conclusion: Neuro-Fuzzy expert system with the highest level of accuracy and a Decision-Table with the lowest Mean absolute error, which are rule-based models, are the best models. Therefore, these models are recommended as a valuable tool for prediction parameters of ICU as well as medical decision-making.   https://ijph.tums.ac.ir/index.php/ijph/article/view/22473 https://ijph.tums.ac.ir/index.php/ijph/article/download/22473/7839