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Showing 5 results for Shahsavari
Management and outcome of prolonged pregnancies in Shariati university hospital
Eslamian L, Shahsavari H,
Volume 65, Issue 12 (2 2008)
Abstract

Background: There is dearth of reports from Iran regarding the prevalence of postterm pregnancy and its complications. The present study was conducted to evaluate the prevalence, management and outcome of prolonged pregnancies.

Methods: This cross-sectional study included data from the hospital records of all women referred to Shariati Hospital, Tehran, from 2001 to 2002 with pregnancies of more than 40 weeks in duration. Pregnancies ≥40-42 weeks were considered postdate and those more than 42 weeks postterm pregnancy. The data compiled from the hospital records were subjected to t, χ2 and Mann-Whitney U tests.

Results: Of the 1500 deliveries in this hospital, 98 patients were included in this study, 66.3% of whom were nullipara and 33.7% multipara. The prevalence of postterm pregnancy was estimated to be 3.3%. Cervix dilation of 2 cm or less on admission occurred in 65 women (73.3%). The mean Bishop score was 4.31. Of the 62 fetuses that underwent assessment tests, 54 (87.1%) were normal. The median time between the last test and induction of labor was 2.1 days, and 2.6 days for cesarean deliveries, which was not a significant difference (P=0.6). Cervical ripening with misoprostrol was performed in 36 cases (36.7%) and was successful in 18 cases. In this group, the median time for cervical ripening in multiparas was significantly less than nulliparas (4 vs. 7 hrs, P=0.004). Women not subjected to cervical ripening had a higher cesarean rate than those who did undergo cervical ripening (74.7% vs. 66.1%), although this difference was not significant (P=0.9). Vaginal and cesarean delivery rates showed no significant difference between cases that underwent induction with oxytocin and those subjected to cervical ripening with misoprostol (P=0.9). The mean Apgar score was 9.5, with all scores above 6. There were no cases of neonatal hypoglycemia, hypocalcemia, NICU admission or prenatal death. The mean nursery stay was 1.84 days with a range of 1-8 days.

Conclusions: The prevalence of postterm pregnancies was 3.3% in this study, due in part to erroneous estimation of gestational age. Sonography exam in the first half of pregnancy can provide a better estimation of gestational age and thereby reduce the rate of postterm pregnancy. Cervical ripening and induction of labor shorten the duration of pregnancy however, whether it has any beneficial effect on neonatal outcome remains controversial.


Designing an intelligent system for diagnosing type 2 diabetes using the data mining approach: brief report
Rohollah Kalhor , Asghar Mortezagholi , Fatemeh Naji, Saeed Shahsavari, Mohammad Zakaria Kiaei ,
Volume 76, Issue 12 (March 2019)
Abstract
Background: Diabetes mellitus has several complications. The Late diagnosis of diabetes in people leads to the spread of complications. Therefore, this study has been done to determine the possibility of predicting diabetes type 2 by using data mining techniques.
Methods: This is a descriptive-analytic study that was conducted as a cross-sectional study. The study population included people referring to health centers in Mohammadieh City in Qazvin Province, Iran, from April to June 2015 for screening for diabetes. The 5-step CRISP method was used to implement this study. Data were collected from March 2015 to June 2015. In this study, 1055 persons with complete information were included in the study. Of these, 159 were healthy and 896 were diabetic. A total of 11 characteristics and risk factors were examined, including the age, sex, systolic and diastolic blood pressure, family history of diabetes, BMI, height, weight, waistline, hip circumference and diagnosis. The results obtained by support vector machine (SVM), decision tree (DT) and the k-nearest neighbors algorithm (k-NN) were compared with each other. Data was analyzed using MATLAB® software, version 3.2 (Mathworks Inc., Natick, MA, USA).
Results: Data analysis showed that in all criteria, the best results were obtained by decision tree with accuracy (0.96) and precision (0.89). The k-NN methods were followed by accuracy (0.96) and precision (0.83) and support vector machine with accuracy (0.94) and precision (0.85). Also, in this study, decision tree model obtained the highest degree of class accuracy for both diabetes classes and healthy in the analysis of confusion matrix.
Conclusion: Based on the results, the decision tree represents the best results in the class of test samples which can be recommended as a model for predicting diabetes type 2 using risk factor data.

Prognosis of multiple sclerosis disease using data mining approaches random forest and support vector machine based on genetic algorithm
Amir Hossein Hashemian , Sara Manochehri , Daryoush Afshari , Zohreh Manochehri , Nader Salari , Soodeh Shahsavari,
Volume 77, Issue 1 (April 2019)
Abstract
Background: Multiple sclerosis (MS) is a degenerative inflammatory disease which is most commonly diagnosed by magnetic resonance imaging (MRI). But, since the MRI device uses of a magnetic field, if there are metal objects in the patient's body, it can disrupt the health of the patient, the functioning of the MRI, and distortion in the images. Due to limitations of using MRI device, screening seems necessary for those patients who have metal objects in their bodies. Therefore, this study is carried out to compare two models: support vector machine and random forest.
Methods: This analytical-modelling research was implemented on MS data collection, the specifications of which are recorded in health registry system in School of Public Health, Kermanshah University of Medical Sciences, Iran, from May 2017 to August 2018. For the purpose of this study, a total of 317 subjects were selected as a sample; 188 subjects were diagnosed with MS and 128 subjects showed no symptoms of MS. In order to fit the support vector machine (SVM) model, radial basis kernel function was used. The parameters of this machine were optimized with genetic algorithm. After this step, the support vector machine and random forest (RF) were compared with respect to three factors: accuracy, sensitivity, and specificity.
Results: Based upon the obtained results of study, accuracy, sensitivity, and specificity of SVM were 0.79, 0.80, and 0.78, respectively. In comparison, accuracy, sensitivity, and specificity of RF were found to be 0.76, 0.81, and 0.70, respectively.
Conclusion: In general, both models which were compared in current study showed desirable performance; however, in term of accuracy, as an important criteria for performance comparison in this area of research, it can be argued that support vector machine can do better than random forest in diagnosing multiple sclerosis.

Comparison of gestational diabetes prediction with artificial neural network and decision tree models
Mansour Rezaei, Negin Fakhri , Fateme Rajati , Soodeh Shahsavari ,
Volume 77, Issue 6 (September 2019)
Abstract
Background: Gestational diabetes mellitus (GDM) is one of the most common metabolic disorders in pregnancy, which is associated with serious complications. In the event of early diagnosis of this disease, some of the maternal and fetal complications can be prevented. The aim of this study was to early predict gestational diabetes mellitus by two statistical models including artificial neural network (ANN) and decision tree and also comparing these models in the diagnosis of GDM.
Methods: In this modeling study, among the cases of pregnant women who were monitored by health care centers of Kermanshah City, Iran, from 2010 to 2012, four hundred cases were selected, therefore the information in these cases was analyzed in this study. Demographic information, mother's maternal pregnancy rating, having diabetes at the beginning of pregnancy, fertility parameters and biochemical test results of mothers was collected from their records. Perceptron ANN and decision tree with CART algorithm models were fitted to the data and those performances were compared. According to the accuracy, sensitivity, specificity criteria and surface under the receiver operating characteristic (ROC) curve (AUC), the superior model was introduced.
Results: Following the fitting of an artificial neural network and decision tree models to data set, the following results were obtained. The accuracy, sensitivity, specificity and area under the ROC curve were calculated for both models. All of these values were more in the neural network model than the decision tree model. The accuracy criterion for these models was 0.83, 0.77, the sensitivity 0.62, 0.56 and specificity 0.95, 0.87, respectively. The surface under the ROC curve in ANN model was significantly higher than decision tree (0.79, 0.74, P=0.03).
Conclusion: In predicting and categorizing the presence and absence of gestational diabetes mellitus, the artificial neural network model had a higher accuracy, sensitivity, specificity, and surface under the receiver operating characteristic curve than the decision tree model. It can be concluded that the perceptron artificial neural network model has better predictions and closer to reality than the decision tree model.

Short term effect of low-level laser therapy on tooth stability after orthodontic treatment
Batoolalsadat Mousavi Fard , Samaneh Sadeghi, Mehrdad Shahsavaripour,
Volume 80, Issue 9 (December 2022)
Abstract
Background: The purpose of this article was comparing the clinical effectiveness of low-level laser therapy (LLLT) in reducing relapse.
Methods: In this clinical trial study 14 patients (11 females and three males) who were under non-extraction treatment (MBT 022 slot) and at the finishing stage of orthodontic treatment at Orthodontics Department of Kerman Dental Faculty from April 2016 to June 2017 participated. Treatment time was two year and the patients at the finishing stage of orthodontic treatment were divided into two groups (RCT code IRCT2017053034061N1). Group 1 (study) were treated with a low-level Gallium aluminum-arsenide diode laser and group 2: control. The exclusion criteria involved patients who consumed medicine that interrupted bone metabolism and those with conditions for which laser therapy could be contraindicated. The laser apparatus emitted a wavelength of 810 nm about 50 seconds and operated with maximum power of 200 MW in continuous wave mode (200 mW, 50 seconds radiation to mesiolingual, mesiobuccal, distolingual and distobuccal surfaces, 35.7 J/cm2). An alginate impression was made from maxillary arch for all patients immediately, four, five and six months after removing the orthodontic archwire and braces and study casts were prepared. The little irregularity index of anterior maxillary arch was measured on the dental casts, with a 0.01 mm precision digital caliper. Intergroup comparisons were performed with Student's t-test and repeated measure ANOVA was perform to compare measurements among groups in different times. The significance level was considered at P<0.05.
Results: There was significant difference among the irregularity index at five and six months after orthodontic treatment between two groups (P<0.05). In the control group except between five and six months after treatment, there was significant difference in irregularity index. The relapse was higher immediately and after four months in the laser group compared to other sequences (P=0.0001).
Conclusion: Sample showed that Low-level laser therapy (LLLT) is a non invasive method for reducing relapse after orthodontic treatment.

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