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Showing 11 results for Classification
Pathological and clinical characteristics of 84 cases of non-Hodgkin's lymphoma
Saljooghi N, Norouzi Z, Hashtroudizad H,
Volume 58, Issue 3 (6-2000)
Abstract

Non-Hodgkin's lymphoma is the 3rd most common cancers in children. In the present study, to determine pathological and clinical features of this cancer, we reviewed records of 84 cases of non-Hodgkin's lymphoma who were admitted to Ali Asghar and Bahrami children hospitals from 1989 to 1996. 59% of cases had small non-cleaved cell (SNCC) subtype of disease. 15% were lymphoblastic and 5% diffuse large cell subtype. The most prevalent primary sites were abdomen and lymph nodes. The most prevalent symptoms were abdominal mass (34%), abdominal pain (37%) and cervcal lymphadenopathy (26%). Over half of our patients were small non-cleaved cell subtype, and further studies should be done to find out reasons for this unusual finding.

 

 


Multinomial response models, for modeling and determining important factors in different contraceptive methods in women
Khalkhali H, Haji Nejad E, Mohammad K,
Volume 59, Issue 1 (4-2001)
Abstract
Difference aspects of multinomial statistical modelings and its classifications has been studied so far. In these type of problems Y is the qualitative random variable with T possible states which are considered as classifications. The goal is prediction of Y based on a random Vector X ? IR^m. Many methods for analyzing these problems were considered. One of the modern and general method of classification is Classification and Regression Trees (CART). Another method is recursive partitioning techniques which has a strange relationship with nonparametric regression. Classical discriminant analysis is a standard method for analyzing these type of data. Flexible discriminant analysis method which is a combination of nonparametric regression and discriminant analysis and classification using spline that includes least square regression and additive cubic splines. Neural network is an advanced statistical method for analyzing these types of data. In this paper properties of multinomial logistics regression were investigated and this method was used for modeling effective factors in selecting contraceptive methods in Ghom province for married women age 15-49. The response variable has a tetranomial distibution. The levels of this variable are: nothing, pills, traditional and a collection of other contraceptive methods. A collection of significant independent variables were: place, age of women, education, history of pregnancy and family size. Menstruation age and age at marriage were not statistically significant.
Job stress and its related factors in Tehran firefighters in year 2000
Sm Yazdi, Sa Sharifian,
Volume 59, Issue 6 (11-2001)
Abstract
Job stress results from a mismatch between job requirements and capabilities, resources, or needs of the worker. Physiological, psychological and behavioral outcomes caused by job stress not only hurt the person but also impose expensive costs on organizations. Firefighting is a job that exposes workers to job stress. The purpose of this study is to determine the level of job stress and some related factors in firefighters of Tehran safety services and firefighters organization. This cross sectional study includes 155 male firefighters whom had selected randomly. In this research we used Leiden University Questionnaire. Also Karasek Questionnaire is used for classification of workers according to karasek’s model. Collected data were analyzed by spss9 software. The final grade of firefighter’s job stress shows a significant positive relationship with second job and a significant negative relationship with age. The level of job satisfaction have a significant negative relationship with job insecurity and lack of meaningfulness, and a significant positive relationship with skill discretion, social support supervisor and social support co-workers. According to karasek classification this job is grouped in active not in high strain grup. The highest level of job stress was seen in physical exertion and hazardous exposure factors. Also in work and time pressure factor, job stress level is high. But job stress is in a moderate or low level in other factors. The level of job stress in younger firefighters and in individuals with a second job indicate a significant increase. However, in western country’s studies, this job is classified as high-strain but in this research it is classified as active group.
Evaluation of survival rate and effective factors in acute myocardial infarction patients in Emam Hospital (Year 2000)
Dabiran S, Maghsoodloo M, Nabaei B,
Volume 60, Issue 4 (7-2002)
Abstract

Introduction: For the time being we have considered that the myocardial infarction is an increasing event in Islamic Republic of Iran and there are many procedures and methods which can help us to diminish the number of death from this ongoing event. The main aim of this research is to determine the survival rate in those patients who have had acute myocardial infarction and the association of it with different variables.

Methods and Materials: The present research is a descriptive case-series study which evaluates the 100 cases of acute myocardial infarction who had been admitted in Tehran Emam Khomeini Hospital during the year 1999.

Results: The mean age of patients was 57 years. The peak of attack rates was in spring and autumn. Investigating of the past history of these patients reviled that 41 percent had been smokers, 63.5 percent have had the history of previous ischemic heart disease, 41 percent have had hyper cholestrolemia, 34 percent had hypertension, 18 percent had diabetes mellitus, 9 percent had mitral rigurgitation and 9 percent had heart block. The Survival rate in our study has been calculated 68 percent in first 28 days of disease.

Conclusion: In our study we concluded that there is significant correlation between survival rate and past history of hypertension, ischemic heart disease, tobacco smoking and clip classification.


HPV infection among patients with high grade cervical intraepithelial neoplasia and squamous cell carcinoma of cervix
Yarandi F, Izadi Mood N, Eftekhar Z, Niakan R, Tajziachi S,
Volume 65, Issue 14 (3-2008)
Abstract
Background: Cervical cancer is the second most common cancer of the women worldwide. It is also an important cause of cancer-related mortality in women, after breast cancer. Nearly half million of new cases are identified yearly. The incidence rate in developing countries is greater than the developed countries. Epidemiologic studies have shown that the association of genital human papilloma virus (HPV) with cervical cancer is strong, independent of other risk factors, and consistent in several countries. The aim of this study was to determine the frequency of HPV in patients with high grade cervical intraepithelial neoplasia (CINIII, CIN II) and squamous cell carcinoma (SCC) of cervix.
Methods: Hundred specimens from patients with SCC and CINIII, CIN II, confirmed by histological review, referring to Mirza Koochak Khan Hospital from 1999-2004 were enrolled in a cross sectional study. Polymerase chain reaction was utilized for identification and typing of HPV DNA. To increase the sensitivity of HPV detection, nested PCRs were performed using MY09/MY11 as outer and GP5/GP6 as inner primers.
Results: It was possible to extract 77 of 100 specimens that HPV DNA was detected in 47 of 77 specimens. Infection with HPV was present in 32 specimens (86.5%) among SCC patients and in 15 specimens (37.5%) among CINIII, CIN II patients. The most frequent HPV types in SCC patients were HPV 16 and 18 (59.38%) and then 33 (34.38%) and in CINIII, CIN II patients was 16 (53.33%) and 18 (40%). the most frequent co-infection in both groups was HPV 16 and 18 which was present in 40.62% and 26.7% of cases respectively.
Conclusions: The most frequent HPV types in patients with SCC and CINIII, CIN II was 16 and 18 that is identical to many other countries infection pattern.
Diffuse large B-Cell lymphoma: a clinico- pathologic and prognostic study on 1470 biopsy specimens
Sadighi S, Tirgary F, Raafat J, Mohagheghi Ma, Safavi S, Vaziri S,
Volume 67, Issue 8 (11-2009)
Abstract

Normal 0 false false false EN-US X-NONE AR-SA MicrosoftInternetExplorer4 Background: Diffuse large B Cell lymphoma (DLBCL) is the most common subtype of non-Hogkin lymphoma (NHL). We performed a retrospective study of patients with de novo DLBCL treated in the Medical Oncology department of Cancer Institute of Iran, Tehran to assess the clinicopathologic and immunohistochemistry correlation and prognosis of the patients.
Methods: World Health Organization (WHO) classification was used to reexamine 1470 biopsy specimens related to the years 1985-2006. After excluding five cases of T Cell large cell lymphoma, 50 Patients diagnosed as DLBCL.
Results: Median age of the patients was 45.5(20-85) years: 60% were male and 30% had primary extranodal disease. The most common extranodal sites were bone, gastrointestinal tract and Head and neck areas. The most common stages were stage II (32%), stage III (32%), stage IV (20%) and stage I (16%) retrospectively and 33% had B-symptoms. All of The Patients received chemotherapy (83% CHOP regimen) and 46% treated by radiotherapy after chemotherapy. With a mean follow up time of 32 months, median survival time was 34 (95% CI 24-40) months. Prognostic factors for survival were tumor stage, B-symptoms and early relapse (less than 6 months).
Conclusions: Our data showed the importance of Immunohistochemistry method in diagnosis of DLBCL. Although DLBCL is potentially curable with CHOP chemotherapy protocol, addition of monoclonal antibody (Anti CD20) and finding new prognostic factors to predict early relapse are clearly needed in Iran.


Analyzing clinical symptoms in multiple sclerosis using data mining
Zahra Raeisi , Pantea Ramezannezad , Marzieh Ahmadzade , Shahram Tarahomi ,
Volume 75, Issue 1 (4-2017)
Abstract

Background: One of the today most common and incurable diseases that is associated with central neural system is ‘MS’ disease. Multiple sclerosis (MS) is a demyelinating disease in which the insulating covers of nerve cells in the brain and spinal cord are damaged. In this disease become apparent a wide spectrum of symptoms such as lose muscles control and their coordination and vision derangement. The goal of this research is to consider to two problems: 1- Recognition of effective clinical symptoms on MS disease and 2- Considering levels of effectiveness of age, sex and education levels factors on MS disease and association between these factors according to verity of categories of this disease.

Methods: Data mining science in medicine is worthy of attention with main application in diagnosis, therapy and prognosis, respectively high volume of collected datum. The data that were used in this article are about patients of Chaharmahal and Bakhtiari Province and collected by cure assistance. In this paper classification and association methods in software engineering field are used. Classification is a general process related to categorization, the process in which ideas and objects are recognized, differentiated, and understood. Association rules are created by analyzing data for frequent if/then patterns and using the criteria support and confidence to identify the most important relationships.

Results: In consideration of first problem in this paper, concluded vision-clinical symptoms are the most effective symptoms and in consideration of second problem, concluded that from 584 records, women affected four times more than men. In other word 70% of MS patients with high graduate are in relapsing-remitting category and 62.5% of MS patients are 20-40 years old.

Conclusion: Some of symptoms are quite temporary and transitory and are ignored by people. Awareness of clinical-symptoms prevalence manner can be warning for people before starting critical cycle of illness. This would cause early diagnosis, effective therapy and even prevention of disease progress, respectively to MS chronicity.

Model of hierarchical self-organizing neural networks for detecting and classifying diabetic retinopathy
Hossein Ghayoumi Zadeh, Mostafa Danaeian, Ali Fayazi , Farshad Namdari, Sayed Mohammad Mostafavi Isfahani ,
Volume 76, Issue 1 (4-2018)
Abstract
Background: One common symptom of diabetes is diabetic retinopathy, if not timely diagnosed and treated, leads to blindness. Retinal image analysis has been currently adopted to diagnose retinopathy. In this study, a model of hierarchical self-organized neural networks has been presented for the detection and classification of retina in diabetic patients.
Methods: This study is a retrospective cross-sectional, conducted from December to February 2015 at the AJA University of Medical Sciences, Tehran. The study has been conducted on the MESSIDOR base, which included 1200 images from the posterior pole of the eye. Retinal images are classified into 3 categories: mild, moderate and severe. A system consisting of a new hybrid classification of SOM has been presented for the detection of retina lesions. The proposed system includes rapid preprocessing, extraction of lesions features, and finally provision of a classification model. In the preprocessing, the system is composed of three processes of primary separation of target lesions, separation of the optical disk, and separation of blood vessels from the retina. The second step is a collection of features based on various descriptions, such as morphology, color, light intensity, and moments. The classification includes a model of hierarchical self-organized networks named HSOM which is proposed to accelerate and increase the accuracy of lesions classification considering the high volume of information in the feature extraction.
Results: The sensitivity, specificity and accuracy of the proposed model for the classification of diabetic retinopathy lesions is 98.9%, 96.77%, 97.87%, respectively.
Conclusion: These days, the cases of diabetes with hypertension are constantly increasing, and one of the main adverse effects of this disease is related to eyes. In this respect, the diagnosis of retinopathy, which is the same as identification of exudates, microanurysm and bleeding, is of particular importance. The results show that the proposed model is able to detect lesions in diabetic retinopathy images and classify them with an acceptable accuracy. In addition, the results suggest that this method has an acceptable performance compared to other methods.

EMG-based wrist gesture recognition using a convolutional neural network
Ali Ameri ,
Volume 77, Issue 7 (10-2019)
Abstract
Background: Deep learning has revolutionized artificial intelligence and has transformed many fields. It allows processing high-dimensional data (such as signals or images) without the need for feature engineering. The aim of this research is to develop a deep learning-based system to decode motor intent from electromyogram (EMG) signals.
Methods: A myoelectric system based on convolutional neural networks (CNN) is proposed, as an alternative to conventional classification methods that depend on feature engineering. The proposed model was validated with 10 able-bodied subjects during single and combined wrist motions. Eight EMG channels were recorded using eight pairs of surface electrodes attached around the subject’s dominant forearm. The raw EMG data from windows of 167ms (200 samples) in 8 channels were arranged as 200×8 matrices. For each subject, a CNN was trained using the EMG matrices as the input and the corresponding motion classes as the target. The resulting model was tested using a 4-fold cross-validation. The performance of the proposed approach was compared to that of a standard SVM-based model that used a set of time-domain (TD) features including mean absolute value, zero crossings, slope sign changes, waveform length, and mean frequency.
Results: In spite of the proven performance and popularity of the TD features, no significant difference (P=0.19) was found between the classification accuracies of the two methods. The advantage of the proposed model is that it does not need manual extraction of features, as the CNN can automatically learn and extract required representations from the EMG data.
Conclusion: These results indicate the capacity of CNNs to learn and extract rich and complex information from biological signals. Because both amplitude and frequency of EMG increases with increasing muscle force, both temporal and spectral characteristics of EMG are needed for efficient estimation of motor intent. The TD set, also includes these types of features. The high performance of the CNN model shows its capability to learn temporal and spectral representations from raw EMG data.

The prediction of lymphedema via the combination of the selected data mining algorithms
Anaram Yaghoobi Notash , Peiman Bayat, Shahpar Haghighat, Ali Yaghoobi Notash ,
Volume 79, Issue 11 (2-2022)
Abstract
Background: Breast cancer is the second leading cause of cancer death in women, after lung cancer. Due to the importance of predicting this disease, the use of data mining methods in medical research is more significant than before. Data mining algorithms can be a great help in preventing the development of lymphedema in patients. The aim Of this study was to create a diagnosis system that can predict the probability of lymphedema in breast cancer patients.
Methods: In the present study, the factors of lymphedema in 1117 patients with breast cancer have been collected. The likelihood of developing lymphedema is predicted using ensemble learning via 5 heterogeneous classification algorithms, feature selection and the genetic algorithm (The Two-layer Ensemble Feature Selection method). After collecting the data of patients with breast cancer from 2009 to 2018, and data preprocessing using the optimized ensemble learning algorithm and feature selection, we will examine the likelihood of developing lymphedema for the new patient. Finally, the factors affecting the disease have been extracted. Excluding the time of collecting statistical data, the period of the study was from September 2019 to February 2021. This study is performed at Seyed Khandan Rehabilitation Center, Tehran, Iran.
Results: The results of algorithms showed that the accuracy of the ensemble learning method with selected classification algorithms (SVM with RBF kernel) is 87% and the accuracy of the ensemble learning with feature selection method is 90%. According to the final evaluation of the proposed method, the most effective risk factors for lymphedema have been extracted.
Conclusion: Unfortunately, treatment and diagnosis are not without complications, and one of the most important of these complications in breast cancer is lymphedema in the upper extremities, which can affect the quality of life in patients. It is essential to have a method that can accurately suggest to a specialist whether a new patient will develop lymphedema in the future or how likely it is to develop it, using patient’s own clinical and demographic characteristics.
 

Deep learning approach for motor imagery EEG signal classification
Faezeh Moghadas, Zahra Amini, Rahele Kafieh,
Volume 80, Issue 10 (1-2023)
Abstract
Background: Brain-computer interface systems provide the possibility of communicating with the outside world without using physiological mediators for people with physical disabilities through brain signals. A popular type of BCIs is the motor imagery-based systems and one of the most important parts in the design of these systems is the classification of brain signals into different motor imagery classes in order to transform them into control commands. In this paper, a new method of brain signal classifying based on deep learning methods is presented.
Methods: This cross-sectional study was conducted at Isfahan University of Medical Sciences, School of Advanced Technologies in Medicine, from February 2020 to June 2022. In the pre-processing block, segmentation of brain signals, selection of suitable channels and filtering by Butterworth filter have been done; then data has transformed to the time-frequency domain by three different kinds of mother wavelets including Cmor, Mexicanhat, and Cgaus. In the classification step, two types of convolutional neural networks (one-dimensional and two-dimensional) were applied whereas each one of them was utilized in two different architectures. Finally, the performance of the networks has been investigated by each one of these three types of input data.
Results: Three channels were selected as the best ones for nine subjects. To separate 8-30 Hz, a 5th degree Butterworth filter was used. After finding the optimal parameters in the proposed networks, wavelet transform with Cgauss mother wavelet has the highest percentage in the both proposed architectures. Two-dimensional convolutional neural network has higher convergence speed, higher accuracy and more complexity of calculations. In terms of accuracy, precision, sensitivity and F1-score, two-dimensional convolutional neural network has performed better than one-dimensional convolutional neural network. The accuracy of 92.53%, which is obtained from the second architecture, as the best result, is reported.
Conclusion: The results obtained from the proposed network indicate that suitable, and well-designed deep learning networks can be utilized as an accurate tool for data classification in application of motion perception.

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