Tehran University Medical Journal

Background: Ensuring adequate depth of anesthesia during surgery is essential for anesthesiologists to prevent the occurrence of unwanted alertness during surgery or failure to return to consciousness. Since the purpose of using anesthetics is to affect the central nervous system, brain signal processing such as electroencephalography (EEG) can be used to predict different levels of anesthesia. Anesthesia disrupts the interaction between different regions of the brain, so brain connectivity between different areas can be a key factor in the anesthesia process. This study aims to determine the depth of anesthesia based on the EEG signal using the effective brain connectivity between frontal and temporal regions.
Methods: This study, which is done from April to December 2018 in Tehran, used EEG signals recorded from eight patients undergoing Propofol anesthesia at Waikato Hospital of New Zealand. In this study, effective brain connectivity in the frontal and temporal regions have been extracted by using various Granger causality methods, including directional transfer function, normalized directional transfer function, partial coherence, partial oriented coherence, and imaginary coherence. The extraction of effective connectivity indices in three modes (awake, anesthesia and recovery) was calculated using MATLAB software. The perceptron neural network is then used to automatically classify the anesthetic phases (Awake, Anesthesia, and recovery).

Background: Due to the fact that various factors are involved in the development of chronic kidney disease, this disease appears with different clinical and laboratory symptoms. The variety in type and number of clinical symptoms often misguides the treating physician. The aim of this study is to extract the key features of the disease and find the best data mining methods to improve the accuracy of kidney disease diagnosis.
Methods: This cross-sectional study was conducted from September 2021 to March 2023 for 30 months at Rafsanjan Ali Ebn Abi Taleb Hospital. Predictive models were developed and tested using different combinations of disease characteristics and seven data mining methods in RapidMiner Studio software. The limitations of the study are as follows: 1) The models were based on 40-year-old and older patients records, which may limit the generalization of results to a wider age group. 2) Despite the high accuracy and comprehensiveness of the method, the models were based only on the information of kidney disease patients at Ali Ibn Abi Talib Rafsanjan Hospital. 3) The climate parameter has not been considered in the data set of the investigation to discover the hidden relationships of this parameter with the kidney disease.

Results: The results of the experiments in this study showed that the proposed prediction model using the Bayes method and eight identified key features (age, renal biopsy, uremia, sedimentation, albumin, edema, nocturnal enuresis, and urine-specific gravity), can detect kidney disease in people of different clinical characteristics, with 99.38% accuracy. Conclusion: Considering that the early diagnosis of kidney disease and the adoption of appropriate treatment methods can prevent the progression of kidney damage, in this study, an attempt has been made to achieve this goal by using new statistical methods and artificial intelligence techniques. Based on the proposed method and the conducted experiments, the most important features and the best data mining method were obtained, and finally, kidney disease prediction was possible with high accuracy.