mohammad dehghandar

In Persian medicine (PM), pulsology is one of the most important ways of knowing the internal states of the body and diagnosing diseases. On the other hand, the photoplethysmogram (PPG) signal provides significant information about heart function for specialists. In this design, fuzzy systems have been used to correlate features from both PPG signal and PM pulsology. Such a system may help pave the path towards the worldwide accepted paradigm of integrative medicine. Using the information of 64 individuals, a fuzzy system was designed by MATLAB software. First, the information related to age and pulse parameters including frequency, strength, speed, length, and width were acquired by a PM specialist via traditional pulse examination and were considered as Input variables. Subsequently, their PPG curve was also recorded by a PO80 beurer pulse oximeter. Afterward, variables of slope, height, and time of systolic upstroke curve at points of 25%, 50%, 75%, and 100% amplitude of the PPG curve were calculated and were considered as output variables. A system with 236 rules was designed and the error of this system was less than 0.05, which is considered an acceptable estimate.Discussion and This designed pilot system estimates the PPG systolic features using PM pulsology with an error of less than 0.05, which can be used to help improve the clinical skills of PM students and practitioners, and can establish the relationship between PM and common medicine.

‎Artificial neural networks with amazing properties‎, ‎such as universal approximation‎, ‎have been utilized to approximate the nonlinear processes in many fields of applied sciences‎. ‎This work proposes the rough-neural networks (R-NNs) for the one-step ahead prediction of chaotic time series‎. ‎We adjust the parameters of R-NNs using a continuous-time Lyapunov-based training algorithm‎, ‎and prove its stability using the continuous form of Lyapunov stability theory‎. ‎Then‎, ‎we utilize the R-NNs to predict the well-known Mackey-Glass time series‎, ‎and Henon map‎, ‎and compare the simulation results with some well-known neural models‎.

The COVID‑19 has become an important health issue in the world and has endangered human health. The purpose of this research is to use an intelligent system model of adaptive neuro‑fuzzy inference system (ANFIS) using twelve variables of input for the diagnosis of COVID‑19. The evaluation of the model was performed using the information of 500 patients referred to and suspected of the COVID‑19. Three hundred and fifty people were used as training data and 150 people were used as test and validation data. Information on 12 important parameters of COVID‑19 such as fever, cough, headache, respiratory rate, Ct‑chest, medical history, skin rash, age, family history, loss of olfactory sensation and taste, digestive symptoms, and malaise was also reported in patients with severe disease. ANFIS identified COVID‑19 in accuracy, sensitivity, and specificity with more than 95%, 94%, and 95%, respectively, which indicates the high efficiency of the system in the correct diagnosis of individuals. The proposed system accurately detected more than 95% COVID‑19 as well as mild, moderate, and acute severity. Due to the time‑constraint, limitations, and error of COVID‑19 diagnostic tools, the proposed system can be used in high‑precision primary detection, as well as saving time and cost.

The purpose of this study was to provide a fuzzy system for predicting and diagnosing metabolic syndrome (MetS) in preschoolers‎, ‎children‎, ‎and adolescents‎. ‎In this study‎, ‎previous research on the factors affecting metabolic syndrome‎, ‎especially in children‎, ‎and adolescents‎, ‎has been considered‎. ‎After integrating the initial variables‎, ‎a fuzzy system has been designed with 8 data on age‎, ‎waist size‎, ‎systole blood pressure‎, ‎diastole blood pressure‎, ‎body mass index (BMI)‎, ‎waist-to-height ratio‎, ‎nutrition‎, ‎and abdominal obesity‎. ‎Ultimately‎, ‎the system gives us an output that diagnoses the health status of a child or adolescent with MetS or predicts the possibility of a person contracting the disease in the future‎. ‎The system is designed based on the data of 1300 persons participating in the fifth study of the program for monitoring and prevention of non-communicable diseases of children‎, ‎and Adolescents in Tehran and Isfahan provinces that 1050 data were used as training data and 250 data as test data that used to test the rules and output of the system‎. ‎After reviewing the rules and eliminating similar or contradictory rules using their degree calculation‎, ‎finally‎, ‎the system was designed with 45 rules‎, ‎a multiplication inference engine‎, ‎a single fuzzifier‎, ‎and a centroid defuzzifier‎. ‎Then the system was evaluated using the confusion matrix accuracy‎, ‎sensitivity‎, ‎and specificity‎. ‎Our analysis shows that this method‎, ‎with an error rate of less than 4 percent more accurate than other methods‎, ‎can predict and diagnose MetS in children.

Overweight obesity is now so widespread in the world. This study aims to use an artificial neural network modeling tool to develop a predictive model for the diagnosis of obesity in children and adolescents.   Participants consisted of 460 school students, aged 7-18 years, who studied in a national school-based surveillance program (CASPIAN-V). Training network with 10 input variables including: age, sex, weight, height, waist circumference, systolic blood pressure, diastolic blood pressure, body mass index, waist-to-height ratio, physical activity, and with output variable obesity with 17 and 15 hidden neurons for girls and boys was designed.   After designing the network, the value of gradient on the data was 0.0021194 for girls and 0.0031658 for boys. The sensitivity, specificity and accuracy of the neural network were 0.9444, 0.9855, 0.9822, respectively in girls, and 0.9655, 0.9757, 0.9755 in boys;  in all these cases, the designed artificial neural network performed better than waist circumference and body mass index. A review of the final weights of this network showed that the input variable body mass index in girls and the input variable waist-to-height ratio in boys had the most influence in diagnosis of obesity.   Our results show that although body mass index has a better diagnostic performance in determining excess body fat than waist circumference, in boys and girls of both groups, and also in all parameters of sensitivity, specificity and accuracy, the artificial neural network acts better than body mass index and waist circumference, so that with an accuracy of more than 96%, we can detects obesity.

Metabolic Syndrome (MetS) is one of the most common metabolic disordersseen in children and adolescents. In this study, the prevalence of MetS and its related factorsare evaluated using a fuzzy expert system (FES) in a national representative sample of agegroups. The FES is designed based on the data of 800 participants of the fifth study of theprogram for monitoring and prevention of non-communicable diseases among children andadolescents in Iran in 2015. The data of 560 participants were used as training data and 240 astest data were used to test the rules and output of the system. The fuzzy system that has beendesigned includes input data (age, waist, systolic blood pressure, diastolic blood pressure,BMI, waist-to-height ratio, nutrition, and abdominal obesity), and at the end gives us anoutput that diagnoses the health status with MetS or predicts the disease. The analysis shows that this method, with an accuracy of more than 98%, can predictand diagnose MetS among children and adolescents better than other methods. The fuzzy system is designed to accept multiple variables simultaneously asinput variables and also use more people information than similar research as primary data.In addition, its accuracy is more than 98%. Preliminary data were collected from children andadolescents with different lifestyles across the country. This system can act as an assistant inthe service of a specialist doctor to diagnose the disease.

Obesity and hypertension are community health problems. The objective of this study was to diagnose obesity and hypertension in Isfahani students by artificial neural network.

The present study was a diagnostic and predictive one that used the information of 460 students aged 7-18 years old in Isfahan to design a neural network with 11 input variables (age, sex, weight, height, waist circumference, body mass index, waist to height ratio, abdominal obesity, physical activity, genetics, and unhealthy eating behaviors) and three output variables of obesity, systolic blood pressure, and diastolic blood pressure. Conjugate Gradient and Levenberg-Marquardt algorithms were used for network training.

Selected neural network with the Levenberg algorithm has 16 hidden neurons in the diagnosis of obesity and high diastolic blood pressure and 14 hidden neurons in the diagnosis of high systolic blood pressure. The sensitivity, specificity, and accuracy of the network in the diagnosis of obesity were 0.9591, 0.9975, and 0.9934, respectively, and these values were 0.8461, 0.9949, and 0.9739 for high systolic blood pressure and 0.7952, 0.9973, and 0.9609 for high diastolic blood pressure. It was observed that the designed network detects obesity in children and adolescents with a high accuracy of 95% and diagnoses systolic and diastolic blood pressures with a high accuracy of 84% and 79%, respectively.

According to the results, about 83% of obese adolescents have hypertension. Therefore, there it is necessary to design educational programs in the field of behavioral changes, including physical activity along with interventions in nutrition planning for students.

Accurate prediction and diagnosis of COVID-19 disease is very important for everyone, especially for medical professionals. On the other hand, the use of fuzzy systems in medicine is increasing rapidly. In this study, a fuzzy system was designed using the information of 375 patients suspected of having COVID-19 disease who referred to Imam Khomeini(Tehran), Alborz(Karaj) and Kowsar(Karaj) hospitals. For this purpose, 300 people were considered to extract the rules and 75 people were considered as test data. Information on 12 important parameters of COVID-19 disease including fever, cough,  headache, gastrointestinal symptoms,  skin rash,  sense of smell and taste, underlying disease, chest CT, blood oxygen level,  lethargy, age, family history and severity of COVID-19 disease received. The fuzzy expert system was designed with 29 rules after reviewing the rules and removing similar and contradictory rules by using their degree calculation. In this system, by integrating some factors, finally 8 input variables and one output variable were considered that was used by product inference engine, singleton fuzzifier and center average defuzzifier. It was observed that the designed fuzzy expert system provides very good results, so that it detects 93% of Covid-19 disease with high accuracy and also the sensitivity of the system is more than 95% and the specificity of the designed system is more than 87%.

the aim of this study is to evaluate the factors affecting the satisfaction of students in the electronic learning environment and the determination of integrated and integrated models to measure their satisfaction . to achieve this purpose , library and descriptive research method was used . the statistical population of the experts of experts is the experts of the experts in the الکترونیکی of mazandaran , and sample members have been selected purposefully , according to the objectives and questions of the research . based on theoretical literature , and by consulting experts and experts , factors and quality standards of e - learning environment were the main dimensions (technical quality of system and technology infrastructure , educational quality , information quality and content , service quality) and 24 criteria . the weight of each of these indexes in the research community was determined based on the questionnaire questionnaire using fuzzy analytic hierarchy process (خبرگان) after obtaining this information and taking advantage of the experts opinion , the rule base should then be completed and a fuzzy expert system with matlab software was proposed to measure satisfaction .