Comparison of the Performance of Log-logistic Regression and Artificial Neural Networks for Predicting Breast Cancer Relapse

Faradmal, Javad,Soltanian, Ali Reza,Roshanaei, Ghodratollah,Khodabakhshi, Reza,Kasaeian, Amir Asian Pacific Journal of Cancer Prevention 2014 Asian Pacific journal of cancer prevention Vol.15 No.14

Background: Breast cancer is the most common cancers in female populations. The exact cause is not known, but is most likely to be a combination of genetic and environmental factors. Log-logistic model (LLM) is applied as a statistical method for predicting survival and it influencing factors. In recent decades, artificial neural network (ANN) models have been increasingly applied to predict survival data. The present research was conducted to compare log-logistic regression and artificial neural network models in prediction of breast cancer (BC) survival. Materials and Methods: A historical cohort study was established with 104 patients suffering from BC from 1997 to 2005. To compare the ANN and LLM in our setting, we used the estimated areas under the receiver-operating characteristic (ROC) curve (AUC) and integrated AUC (iAUC). The data were analyzed using R statistical software. Results: The AUC for the first, second and third years after diagnosis are 0.918, 0.780 and 0.800 in ANN, and 0.834, 0.733 and 0.616 in LLM, respectively. The mean AUC for ANN was statistically higher than that of the LLM (0.845 vs. 0.744). Hence, this study showed a significant difference between the performance in terms of prediction by ANN and LLM. Conclusions: This study demonstrated that the ability of prediction with ANN was higher than with the LLM model. Thus, the use of ANN method for prediction of survival in field of breast cancer is suggested.

Correlations between anatomical variations of the nasal cavity and ethmoidal sinuses on cone-beam computed tomography scans

Shokri, Abbas,Faradmal, Mohammad Javad,Hekmat, Bahareh Korean Academy of Oral and Maxillofacial Radiology 2019 Imaging Science in Dentistry Vol.49 No.2

Purpose: Anatomical variations of the external nasal wall are highly important, since they play a role in obstruction or drainage of the ostiomeatal complex and ventilation and can consequently elevate the risk of pathological sinus conditions. This study aimed to assess anatomical variations of the nasal cavity and ethmoidal sinuses and their correlations on cone-beam computed tomography (CBCT) scans. Materials and Methods: This cross-sectional study evaluated CBCT scans of 250 patients, including 107 males and 143 females, to determine the prevalence of anatomical variations of the nasal cavity and ethmoidal sinuses. All images were taken using a New Tom 3G scanner. Data were analyzed using the chi-square test, Kruskal-Wallis test, and the Mann-Whitney test. Results: The most common anatomical variations were found to be nasal septal deviation (90.4%), agger nasi air cell (53.6%), superior orbital cell(47.6%), pneumatized nasal septum(40%), and Onodi air cell(37.2%). Correlations were found between nasal septal deviation and the presence of a pneumatized nasal septum, nasal spur, and Haller cell. No significant associations were noted between the age or sex of patients and the presence of anatomical variations (P>0.05). Conclusion: Radiologists and surgeons must pay close attention to the anatomical variations of the sinonasal region in the preoperative assessment to prevent perioperative complications.

Predicting Hospital Readmission in Heart Failure Patients in Iran: A Comparison of Various Machine Learning Methods

Roya Najafi-Vosough,Javad Faradmal,Seyed Kianoosh Hosseini,Abbas Moghimbeigi,Hossein Mahjub 대한의료정보학회 2021 Healthcare Informatics Research Vol.27 No.4

Objectives: Heart failure (HF) is a common disease with a high hospital readmission rate. This study considered class imbalanceand missing data, which are two common issues in medical data. The current study’s main goal was to compare theperformance of six machine learning (ML) methods for predicting hospital readmission in HF patients. Methods: In thisretrospective cohort study, information of 1,856 HF patients was analyzed. These patients were hospitalized in FarshchianHeart Center in Hamadan Province in Western Iran, from October 2015 to July 2019. The support vector machine (SVM),least-square SVM (LS-SVM), bagging, random forest (RF), AdaBoost, and naïve Bayes (NB) methods were used to predicthospital readmission. These methods’ performance was evaluated using sensitivity, specificity, positive predictive value, negativepredictive value, and accuracy. Two imputation methods were also used to deal with missing data. Results: Of the 1,856HF patients, 29.9% had at least one hospital readmission. Among the ML methods, LS-SVM performed the worst, with accuracyin the range of 0.57–0.60, while RF performed the best, with the highest accuracy (range, 0.90–0.91). Other ML methodsshowed relatively good performance, with accuracy exceeding 0.84 in the test datasets. Furthermore, the performance ofthe SVM and LS-SVM methods in terms of accuracy was higher with the multiple imputation method than with the medianimputation method. Conclusions: This study showed that RF performed better, in terms of accuracy, than other methods forpredicting hospital readmission in HF patients.

Joint Disease Mapping of Two Digestive Cancers in Golestan Province, Iran Using a Shared Component Model

Parisa Chamanpara,Abbas Moghimbeigi,Javad Faradmal,Jalal Poorolajal 질병관리본부 2015 Osong Public Health and Research Persptectives Vol.6 No.3

Objectives: Recent studies have suggested the occurrence patterns and related diet factor of esophagus cancer (EC) and gastric cancer (GC). Incidence of these cancers was mapped either in general and stratified by sex. The aim of this study was to model the geographical variation in incidence of these two related cancers jointly to explore the relative importance of an intended risk factor, diet low in fruit and vegetable intake, in Golestan, Iran. Methods: Data on the incidence of EC and GC between 2004 and 2008 were extracted from Golestan Research Center of Gastroenterology and Hepatology, Hamadan, Iran. These data were registered as new observations in 11 counties of the province yearly. The Bayesian shared component model was used to analyze the spatial variation of incidence rates jointly and in this study we analyzed the data using this model. Joint modeling improved the precision of estimations of underlying diseases pattern, and thus strengthened the relevant results. Results: From 2004 to 2008, the joint incidence rates of the two cancers studied were relatively high (0.8-1.2) in the Golestan area. The general map showed that the northern part of the province was at higher risk than the other parts. Thus the component representing diet low in fruit and vegetable intake had larger effect of EC and GC incidence rates in this part. This incidence risk pattern was retained for female but for male was a little different. Conclusion: Using a shared component model for joint modeling of incidence rates leads to more precise estimates, so the common risk factor, a diet low in fruit and vegetables, is important in this area and needs more attention in the allocation and delivery of public health policies.

Diabetic peripheral neuropathy class prediction by multicategory support vector machine model: a cross-sectional study

Maryam Kazemi,Abbas Moghimbeigi,Javad Kiani,Hossein Mahjub,Javad Faradmal 한국역학회 2016 Epidemiology and Health Vol.38 No.-

OBJECTIVES: Diabetes is increasing in worldwide prevalence, toward epidemic levels. Diabetic neuropathy, one of the most common complications of diabetes mellitus, is a serious condition that can lead to amputation. This study used a multicategory support vector machine (MSVM) to predict diabetic peripheral neuropathy severity classified into four categories using patients’ demographic characteristics and clinical features. METHODS: In this study, the data were collected at the Diabetes Center of Hamadan in Iran. Patients were enrolled by the convenience sampling method. Six hundred patients were recruited. After obtaining informed consent, a questionnaire collecting general information and a neuropathy disability score (NDS) questionnaire were administered. The NDS was used to classify the severity of the disease. We used MSVM with both one-against-all and one-against-one methods and three kernel functions, radial basis function (RBF), linear, and polynomial, to predict the class of disease with an unbalanced dataset. The synthetic minority class oversampling technique algorithm was used to improve model performance. To compare the performance of the models, the mean of accuracy was used. RESULTS: For predicting diabetic neuropathy, a classifier built from a balanced dataset and the RBF kernel function with a one-against-one strategy predicted the class to which a patient belonged with about 76% accuracy. CONCLUSIONS: The results of this study indicate that, in terms of overall classification accuracy, the MSVM model based on a balanced dataset can be useful for predicting the severity of diabetic neuropathy, and it should be further investigated for the prediction of other diseases.

UVA-LED assisted persulfate/nZVI and hydrogen peroxide/nZVI for degrading 4-chlorophenol in aqueous solutions

Abdolmotaleb Seidmohammadi,Raheleh Amiri,Javad Faradmal,Mostafa Lili,Ghorban Asgari 한국화학공학회 2018 Korean Journal of Chemical Engineering Vol.35 No.3

Photocatalytic degradation of 4-chlrophenol (4-CP) using UVA-LED assisted persulfate and hydrogen peroxide activated by the nZVI (Nano Zero Valent Iron) in a batch photocatalytic reactor was investigated. The reaction involved a lab-scale photoreactor irradiated with UVA-LED light emitted at 390 nm. The efficiency of the reaction was evaluted in terms of 4-CP degradation and mineralization degree at different pH of solution, initial concentrations of nZVI, persulfate, hydrogen peroxide and 4-CP. In UVA-LED/H2O2/nZVI process, complete degradation of 4-CP (>99%) and 75% mineralization was achieved at pH of 3, hydrogen peroxide concentration of 0.75 mM, nZVI dosage of 1mM and initial 4-CP concentration of 25mg/L at the reaction time of 30 min. The optimum conditions obtained for the best 4-CP degradation rate were at an initial concentration of 25mg/l, persulfate concentration of 1.5mM, nZVI dosage of 1mM, pH of 3 and reaction time of 120min for UVA-LED/persulfate/nZVI process. It was also observed that the 4-CP degradation rate is dependent on initial 4-CP concentrations for both processes. The pseudofirst- order kinetic constant at 25mg/L initial concentration of 4-CP was found to be 1.4×10−1 and 3.8×10−2 in UVALED/ H2O2/nZVI and UVA-LED/persulfate/nZVI processes, respectively. Briefly, the UVA-LED/H2O2/nZVI process enhanced the degradation rate of 4-CP by 3.67-times in comparison to UVA-LED/persulfate/nZVI process at 30min contact time, which serves as a new and feasible approach for the degradation of 4-CP as well as other organic contaminants containing wastewater.

Correlations between anatomical variations of the nasal cavity and ethmoidal sinuses on cone-beam computed tomography scans

Abbas Shokri,Mohammad Javad Faradmal,Bahareh Hekmat 대한영상치의학회 2019 Imaging Science in Dentistry Vol.49 No.2

Purpose: Anatomical variations of the external nasal wall are highly important, since they play a role in obstruction or drainage of the ostiomeatal complex and ventilation and can consequently elevate the risk of pathological sinus conditions. This study aimed to assess anatomical variations of the nasal cavity and ethmoidal sinuses and their correlations on cone-beam computed tomography (CBCT) scans. Materials and Methods: This cross-sectional study evaluated CBCT scans of 250 patients, including 107 males and 143 females, to determine the prevalence of anatomical variations of the nasal cavity and ethmoidal sinuses. All images were taken using a New Tom 3G scanner. Data were analyzed using the chi-square test, Kruskal-Wallis test, and the Mann-Whitney test. Results: The most common anatomical variations were found to be nasal septal deviation (90.4%), agger nasi air cell (53.6%), superior orbital cell (47.6%), pneumatized nasal septum (40%), and Onodi air cell (37.2%). Correlations were found between nasal septal deviation and the presence of a pneumatized nasal septum, nasal spur, and Haller cell. No significant associations were noted between the age or sex of patients and the presence of anatomical variations (P>0.05). Conclusion: Radiologists and surgeons must pay close attention to the anatomical variations of the sinonasal region in the preoperative assessment to prevent perioperative complications.

Abatement of Cr (VI) from wastewater using a new adsorbent, cantaloupe peel: Taguchi L16 orthogonal array optimization

Bahman Ramavandi,Ghorban Asgari,Javad Faradmal,Soleyman Sahebi,Babak Roshani 한국화학공학회 2014 Korean Journal of Chemical Engineering Vol.31 No.12

Taguchi orthogonal design was applied for multivariate optimization of Cr (VI) abatement by canta-loupe peel powder (CPP), as a novel adsorbent, from industrial wastewater in a batch mode. Effective factors in theadsorption process, such as temperature, CPP dose, Cr (VI) concentration, wastewater pH, and contact time, were con-sidered using an L16 orthogonal array design. The best conditions for adsorbing of Cr (VI) were determined by the Taguchimethod and desirability approach as pH of 2, chromium concentration of 100 mg/L, contact time of 5 min, CPP dosageof 0.5 g/L, and wastewater temperature of 25 oC. Analysis of variance results indicated that the pH was the most im-portant variable influencing the chromium removal percentage, and its contribution value was obtained 45.01%. TheLangmuir model proved best fit for the experimental data and maximum adsorption capacity of Cr (VI) onto CPP wasobtained 166.25 mg/g. The final part of the study includes an examination of the CPP through an analysis of the removalof chromium from real industrial wastewater. It can be concluded that the CPP presents a promising and efficient al-ternative for eliminating of Cr (VI) from industrial wastewaters.

Prediction of Serum Creatinine in Hemodialysis Patients Using a Kernel Approach for Longitudinal Data

Mohammad Moqaddasi Amiri,Leili Tapak,Javad Faradmal,Javad Hosseini,Ghodratollah Roshanaei 대한의료정보학회 2020 Healthcare Informatics Research Vol.26 No.2

Objectives: Longitudinal data are prevalent in clinical research; due to their correlated nature, special analysis must be used for this type of data. Creatinine is an important marker in predicting end-stage renal disease, and it is recorded longitudinally. This study compared the prediction performance of linear regression (LR), linear mixed-effects model (LMM), leastsquares support vector regression (LS-SVR), and mixed-effects least-squares support vector regression (MLS-SVR) methods to predict serum creatinine as a longitudinal outcome. Methods: We used a longitudinal dataset of hemodialysis patients in Hamadan city between 2013 and 2016. To evaluate the performance of the methods in serum creatinine prediction, the data was divided into two sets of training and testing samples. Then LR, LMM, LS-SVR, and MLS-SVR were fitted. The prediction performance was assessed and compared in terms of mean squared error (MSE), mean absolute error (MAE), mean absolute prediction error (MAPE), and determination coefficient (R2). Variable importance was calculated using the best model to select the most important predictors. Results: The MLS-SVR outperformed the other methods in terms of the least prediction error; MSE = 1.280, MAE = 0.833, and MAPE = 0.129 for the training set and MSE = 3.275, MAE = 1.319, and MAPE = 0.159 for the testing set. Also, the MLS-SVR had the highest R2, 0.805 and 0.654 for both the training and testing samples, respectively. Blood urea nitrogen was the most important factor in the prediction of creatinine. Conclusions: The MLS-SVR achieved the best serum creatinine prediction performance in comparison to LR, LMM, and LS-SVR.

Gastric and Esophageal Cancers Incidence Mapping in Golestan Province, Iran: Using Bayesian-Gibbs Sampling

Atefeh-Sadat Hosseintabar Marzoni,Abbas Moghimbeigi,Javad Faradmal 질병관리본부 2015 Osong Public Health and Research Persptectives Vol.6 No.2

Objectives: Recent studies of esophageal cancer (EC) and gastric cancer (GC) have been reported to have high incidence rates of these cancers in Golestan Province of Iran. The present study describes the geographical patterns of EC and GC incidence based on cancer registry data and display statistically significant regions within this province. Methods: In order to map the distribution of upper gastrointestinal cancer, relative risk (RR) were calculated. Therefore, to estimate a more reliable RR, Poisson regression models were used. The adjusted models (adjusted to urban-rural area, sex, and grouped age proportion) were utilized. We considered twocomponent random effects for each observation, an unstructured (noncorrelated) and a group of “neighbor” (correlated) heterogeneities. We estimated the model parameters using Gibbs sampling and empirical Bayes method. We used EC and GC data that were registered with Golestan Research Center of Gastroenterology and Hepatology in the years 2004-2008. Results: The EC and GC maps were drawn for 2004-2008 in the province. Kalaleh and Minoodasht counties have a high RR of EC and GC in the years of study. In almost all years, the areas with a high RR were steady. Conclusion: The EC and GC maps showed significant spatial patterns of risk in Golestan province of Iran. Further study is needed to multivariate clustering and mapping of cancers RRs with considering diet and socioeconomic factors.