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Showing 7 results for Neural Networks
Application of artificial neural network to predict graft survival after kidney transplantation: Reports of 22 years follow up of 316 patients in Isfahan
Ashrafi M, Hamidi Beheshti Mt, Shahidi Sh, Ashrafi F,
Volume 67, Issue 5 (8-2009)
Abstract

Background: Kidney transplantation had been evaluated in some researches in Iran mainly with clinical approach. In this research we evaluated graft survival in kidney recipients and factors impacting on survival rate. Artificial neural networks have a good ability in modeling complex relationships, so we used this ability to demonstrate a model for prediction of 5yr graft survival after kidney transplantation.
Methods: This retrospective study was done on 316 kidney transplants from 1984 through 2006 in Isfahan. Graft survival was calculated by Kaplan-meire method. Cox regression and artificial neural networks were used for constructing a model for prediction of graft survival.
Results: Body mass index (BMI) and type of transplantation (living/cadaver) had significant effects on graft survival in cox regression model. Effective variables in neural network model were recipient age, recipient BMI, type of transplantation and donor age. One year, 3 year and 5 year graft survival was 96%, 93% and 90% respectively. Suggested artificial neural network model had good accuracy (72%) with the area under the Receiver-Operating Characteristic (ROC) curve 0.736 and appropriate results in goodness of fit test (κ2=33.924). Sensitivity of model in identification of true positive situations was more than false negative situations (72% Vs 61%).
Conclusion: Graft survival in living donors was more than cadaver donors. Graft survival decreased when the BMI increased at transplantation time. In traditional statistical approach Cox regression analysis is used in survival analysis, this research shows that artificial neural networks also can be used in constructing models to predict graft survival in kidney transplantation.


Designing an artificial neural network for prediction of pregnancy outcomes in women with systemic lupus erythematosus in Iran
Mahmoud Akbarian , Khadijeh Paydar, Sharareh R Ostam Niakan Kalhori , Abbas Sheikhtaheri ,
Volume 73, Issue 4 (7-2015)
Abstract
Background: Pregnancy in women with systemic lupus erythematosus (SLE) is still introduced as a major challenge. Consulting before pregnancy in these patients is essential in order to estimating the risk of undesirable maternal and fetal outcomes by using appropriate information. The purpose of this study was to develop an artificial neural network for prediction of pregnancy outcomes including spontaneous abortion and live birth in SLE. Methods: In a retrospective study, forty-five variables were identified as effective factors for prediction of pregnancy outcomes in systemic lupus erythematosus. Data of 104 pregnancies in women with systemic lupus erythematosus in Shariati Hospital and 45 pregnancies in a private specialized center in Tehran from 1982 to 2014 in August and September, 2014 were collected and analyzed. For feature selection, information of the 149 pregnancies was analyzed with a binary logistic regression model in SPSS software, version 20 (SPSS, Inc., Chicago, IL, USA). These selected variables were used for inputs of neural networks in MATLAB software, version R2013b (MathWorks Inc., Natick, MA, USA). A Multi-Layer Perceptron (MLP) network with scaled conjugate gradient (trainscg) back propagation learning algorithm has been designed and evaluated for this purpose. We used confusion matrix for evaluation. The accuracy, sensitivity and specificity were calculated from the confusion matrix. Results: Twelve features with P<0.05 and four features with P<0.1 were identified by using binary logistic regression as effective features. These sixteen features were used as input variables in artificial neural networks. The accuracy, sensitivity and specificity of the test data for the MLP network were 90.9%, 80.0%, and 94.1% respectively and for the total data were 97.3%, 93.5%, and 99.0% respectively. Conclusion: According to the results, we concluded that feed-forward Multi-Layer Perceptron (MLP) neural network with scaled conjugate gradient (trainscg) back propagation learning algorithm can help physicians to predict the pregnancy outcomes (spontaneous abortion and live birth) among pregnant women with lupus by using identified effective variables.
Effective feature selection of clinical and genetic to predict warfarin dose using artificial neural network
Mohammad Karim Sohrabi , Alireza Tajik ,
Volume 73, Issue 12 (3-2016)
Abstract

Background: Warfarin is one of the most common oral anticoagulant, which role is to prevent the clots. The dose of this medicine is very important because changes can be dangerous for patients. Diagnosis is difficult for physicians because increase and decrease in use of warfarin is so dangerous for patients. Identifying the clinical and genetic features involved in determining dose could be useful to predict using data mining techniques. The aim of this paper is to provide a convenient way to select the clinical and genetic features to determine the dose of warfarin using artificial neural networks (ANN) and evaluate it in order to predict the dose patients.

Methods: This experimental study, was investigate from April to May 2014 on 552 patients in Tehran Heart Center Hospital (THC) candidates for warfarin anticoagulant therapy within the international normalized ratio (INR) therapeutic target. Factors affecting the dose include clinical characteristics and genetic extracted, and different methods of feature selection based on genetic algorithm and particle swarm optimization (PSO) and evaluation function neural networks in MATLAB (MathWorks, MA, USA), were performed.

Results: Between algorithms used, particle swarm optimization algorithm accuracy was more appropriate, for the mean square error (MSE), root mean square error (RMSE) and mean absolute error (MAE) were 0.0262, 0.1621 and 0.1164, respectively.

Conclusion: In this article, the most important characteristics were identified using methods of feature selection and the stable dose had been predicted based on artificial neural networks. The output is acceptable and with less features, it is possible to achieve the prediction warfarin dose accurately. Since the prescribed dose for the patients is important, the output of the obtained model can be used as a decision support system.


A clinical decision support system for contact lens evaluation
Fatemeh Falahati Marvast , Hossein Arabalibeik, Fatemeh Alipour , Abbas Sheikhtaheri, Leila Nouri,
Volume 76, Issue 12 (3-2019)
Abstract
Background: Contact lenses are transparent, thin plastic disks that cover the surface of the cornea. Appropriate lens prescription should be performed properly by an expert to provide better visual acuity and reduce side effects. The lens administration is a multi-stage, complex and time-consuming process involving many considerations. The purpose of this study was to develop a decision support system in the field of contact lens prescription.
Methods: In this fundamental study, data were collected from 127 keratoconus patients referred to the contact lens clinic at Farabi Eye Hospital, Tehran, Iran during the period of March 2013 to July 2014. Five parameters in the contact lens prescribing process were investigated. Parameters were collected as follows. “Lens vertical position”, “vertical movement of the lens during blinking” and “width of the rim” in the fluorescein pattern were obtained by recording videos of the patients while wearing the lens. “Fluorescein dye concentration” under the lens was evaluated by the physician and “patient comfort” was obtained by asking the patient to fill a simple scoring system. Approved and disapproved lenses were judged and recorded based on the decision of an expert contact lens practitioner. The decision support system was designed using artificial neural networks with the mentioned variables as inputs. Approved and disapproved lenses are considered as system outputs. Artificial neural network was developed using MATLAB® software, version 8.3 (Mathworks Inc., Natick, MA, USA). Eighty percent of the data was used to train the support vector machine and the rest of the data (20%) to test the system's performance.
Results: Accuracy, sensitivity and specificity, calculated using the confusion matrix, were 91.3%, 89.8% and 92.6% respectively. The results indicate that the designed decision support system could assist contact lens prescription with high precision.
Conclusion: According to the results, we conclude that hard contact lens fitness could be evaluated properly using an artificial neural network as a decision support system. The proposed system detected approved and disapproved contact lenses with high accuracy.

Tuberculosis incidence predicting system using time series neural network in Iran
Atefeh Sedighnia , Sharareh Rostam Niakan Kalhori, Mahshid Nasehi , Ahmad Ali Hanafi-Bojd ,
Volume 77, Issue 4 (7-2019)
Abstract
Background: Tuberculosis (TB) is an important infectious disease with high mortality in the world. None of the countries stay safe from TB. Nowadays, different factors such as Co-morbidities, increase TB incidence. World Health Organization (WHO) last report about Iran's TB status shows rising trend of multidrug-resistant tuberculosis (MDR-TB) and HIV/TB. More than 95% illness and death of TB cases are in developing countries. The most infections are in South East Asia and West Pacific that 56% of them are new cases in the world. The incidence is actually new cases of each year. Incidence prediction is affecting TB prevention, management and control. The purpose of this study is designing and creating a system to predict TB incidence by time series artificial neural networks (ANN) in Iran.
Methods: This study is a retrospective analytic. 10651 TB cases that registered on Iran’s Stop TB System from March 2014 to March 2016, Were analyzed. Most of reliable data used directly, some of them merged together and create new indicators and two columns used to compute a new indicator. At first, effective variables were evaluating with correlation coefficient tests then extracting by linear regression on SPSS statistical software, version 20 (IBM, Armonk, NY, USA). We used different algorithms and number of neurons in hidden layer and delay in time series neural network. R, MSE (mean squared error) and regression graph were used for compare and select the best network. Incidence prediction neural network were designed on MATLAB® software, version R2014a (Mathworks Inc., Natick, MA, USA).
Results: At first, 23 independent variables entered to study. After correlation coefficient and regression, 12 variables with P≤0.01 in Spearman and P≤0.05 in Pearson were selected. We had the best value of R, MSE (mean squared error) and also regression graph in train, validation and tested by Bayesian regularization algorithm with 10 neuron in hidden layer and two delay.
Conclusion: This study showed that artificial neural network had acceptable function to extract knowledge from TB raw data; ANN is beneficial to TB incidence prediction.

Non-melanoma skin cancer diagnosis with a convolutional neural network
Ali Ameri,
Volume 78, Issue 4 (7-2020)
Abstract
Background: The most common types of non-melanoma skin cancer are basal cell carcinoma (BCC), and squamous cell carcinoma (SCC). AKIEC -Actinic keratoses (Solar keratoses) and intraepithelial carcinoma (Bowen’s disease)- are common non-invasive precursors of SCC, which may progress to invasive SCC, if left untreated. Due to the importance of early detection in cancer treatment, this study aimed to propose a computer-based model for identification non-melanoma malignancies.
Methods: In this analytic study, 327 AKIEC, 513 BCC, and 840 benign keratosis images from human against machine with 10000 training dermoscopy images (HAM10000) were extracted. From each of these three types, 90% of the images were designated as the training set and the remaining images were considered as the test set. A deep learning convolutional neural network (CNN) was developed for skin cancer detection by using AlexNet (Krizhevsky, et al., 2012) as a pretrained network. First, the model was trained on the training images to discriminate between benign and malignant lesions. In comparison with conventional methods, the main advantage of the proposed approach is that it does not need cumbersome and time-consuming procedures of lesion segmentation and feature extraction. This is because CNNs have the capability of learning useful features from the raw images. Once the system was trained, it was validated with test data to assess the performance. Study was carried out at Shahid Beheshti University of Medical Sciences, Tehran, Iran, in January and February, 2020.
Results: The proposed deep learning network achieved an AUC (area under the ROC curve) of 0.97. Using a confidence score threshold of 0.5, a classification accuracy of 90% was attained in the classification of images into malignant and benign lesions. Moreover, a sensitivity of 94% and specificity of 86% were obtained. It should be noted that the user can change the threshold to adjust the model performance based on preference. For example, reducing the threshold increase sensitivity while decreasing specificity.
Conclusion: The results highlight the efficacy of deep learning models in detecting non-melanoma skin cancer. This approach can be employed in computer-aided detection systems to assist dermatologists in identification of malignant lesions.
 

Comparison of disability score estimation in multiple sclerosis patients with artificial neural network and decision tree models
Mansour Rezaei , Daryush Afshari, Negin Fakhri, Nazanin Razazian,
Volume 79, Issue 4 (7-2021)
Abstract
Background: Multiple Sclerosis (MS) is one of the most debilitating disease among young adults. Understanding the disability score (Expanded Disability Status Scale (EDSS)) of these patients is helpful in choosing their treatment process. Calculating EDSS takes a lot of time for Neurologists, so having a way to estimate EDSS can be helpful. This study aimed to estimate the EDSS score of MS patients using statistical models including Artificial Neural Network (ANN) and Decision Tree (DT) models.
Methods: This cross-sectional study was performed on MS registry study data of Kermanshah province from April 2017 to November 2018. From the total data available in the registry system, The 12 variables including demographic information, information about MS disease and their EDSS score were extracted. EDSS scores were also estimated using ANN and DT models. The performance of the models was compared in terms of estimation error, correlation and mean of an estimated score. Data were analyzed using Weka software version 3.9.2 and SPSS software version 25 with a significance level of 0.05.
Results: In this study, 353 people were studied. The mean age of the patients was 36.47±9.1 years, the mean age of onset was 9.2±30.34 years, the mean duration of the disease was 6.20±5.7 years and the mean EDSS score was 2.46±1.8. Estimation errors in the DT model were lower than in the ANN model. The real EDSS score was significantly correlated with scores estimated by DT (r=0.571) and ANN (r=0.623). The mean EDSS estimated by the DT model (2.46±1.1) was not significantly different from the real EDSS mean (P=0.621) but the mean EDSS estimated by the ANN model (2.87±1.3) was significantly higher than the real EDSS mean. (P<0.05).
Conclusion: The DT model could better estimate the EDSS score of MS patients than the ANN model and made predictions that were closer to the actual EDSS scores. Therefore, the DT model can accurately estimate the EDSS score of MS patients.

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