Comparing of Four Ergonomic Risk Assessment Methods of HAL-TLV, Strain Index, OCRA Checklist, and ART for Repetitive Work Tasks

This study is aimed to compare the results obtained from four risk assessment methods, videlicet HAL-TLV, Strain index, OCRA checklist, and ART. These musculoskeletal disorders assessment tools are generally used in the studies as well as in the field of occupational health. In this study, the data was collected via assessments of 30 tasks by 9 raters in poultry slaughter, assembly, and container production industries using four methods of upper limb musculoskeletal disorder risk assessment. In order to determine the level of agreement between the risk assessment methods, the Spearman's rank correlation coefficient and Cohen's weighted kappa were used, according to which the highest agreement and correlation were found between ART and OCRA checklist methods, while the HAL-TLV and OCRA checklist exhibited the lowest agreement and correlation. The difference between the risk classification results of the studied methods could be due to the difference of definitions of the risk variables; therefore, selecting the assessment tools for assessing the task risks in the working environment must be in accordance with the assessment objectives and complexity of the work tasks

A Support Vector Regression Approach for Three–Level Longitudinal Data

Background: Longitudinal data structure is frequently observed in health science. This introduces correlation to the data that needs to be handled in modelling process. Recently, machine learning approaches have been introduced in the context of longitudinal data for prediction of the response variable purpose. In this paper a mixed-effects least squares support vector regression model is presented for three-level longitudinal data. In the proposed model, multiple random-effect terms are used for considering the existing correlation structures in longitudinal data. The proposed model is flexible in modelling (non-)linear and complex relationships between predictors and response, while it takes into account the hierarchical structure of data and is computationally efficient.  Methods Both random intercept and random trend models with a special correlation structure of errors are illustrated. A real data example on human Brucellosis rate is analysed and two simulation studies are performed to illustrate the proposed model. The fitting and generalisation performance of the proposed model are investigated and compared with the ordinary least squares support vector regression and linear mixed-effects models.  Results: Based on the human Brucellosis rate example and two simulation studies, the proposed models had the best performance in generalisation. Also, the fitting performances of the proposed models were better than that of the classic models.  Conclusion: Our study revealed that in the presence of nonlinear relationship between covariates and outcome, the proposed MLS-SVR model has the best fitting and generalisation performance and can capture correlation of the data

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

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

Exploring the spatial patterns of three prevalent cancer latent risk factors in Iran; Using a shared component model

Background and aims: The aim of this study was the modeling of the incidence rates of Colorectal, breast and prostate cancers using a shared component model in order to explore the spatial pattern of their shared risk factors (i.e., obesity and low physical activity) affecting on cancer incidence, and also to estimate the relative weight of these shared components. Methods: In this study, the new cases of colorectal, breast and prostate cancers information provided by the Management Center of Ministry of Health and Medical Education in 2009 were analyzed. The Bayesian shared component model was used. In addition, BYM (Besag, York and Mollie) model was applied to investigate the geographical pattern of disease incidence rates, individually. Results: The larger effect of obesity on the incidence of the relevant cancers was found in Ardabil, West Azarbaijan, Gilan, Zanjan, Kurdistan, Qazvin, Tehran, Mazandaran, Hamadan, Kermanshah, Semnan, Golestan, Yazd and Kerman, and this component was more important for prostate cancer compared to colorectal and breast cancers. In addition, low physical activity shared component had more effect on the incidence of colorectal and breast cancers in Ardabil, Zanjan, Qazvin, Tehran, Mazandaran, Markazi, Lorestan, Kermanshah, Ilam, Khuzestan, South Khorasan, Yazd, Kerman and Fars, and also, this component was more important for Breast cancer compared to Colorectal cancer. Conclusion: Based on deviance Information criterion, combined modeling of three understudy cancers using a shared component model was better than modeling them individually using BYM model

Detecting Rates, Trends and Determinants of Cesarean Section Deliveries in Iran Using Generalized Additive Mixed Models

Background: The present study aimed to investigate the trend of cesarean section and its related factors through the recent years. Methods: The study data containing delivery information from Hamadan hospitals and are recorded from 2001 to 2014. The data were analyzed through the generalized additive mixed models using R software (v. 3.2.2). Results: cesarean rate in this study was about 42%. According to the results, the trend of cesarean deliveries almost increased in the recent years. A significant relationship was found between the average age and elective cesarean rate, but, the pregnancy rate didn’t have a significant effect on the elective cesarean rate. Conclusion: Cesarean section rate was more than the allowed limit by world health organization (WHO) that is 15%. Although cesarean delivery is preferred to natural vaginal delivery in the case the mother’s or infant’s life is in danger, it should not replace natural delivery for any reason. Natural vaginal delivery can be promoted by providing the society with the knowledge about the advantages of natural delivery and complications of cesarean section

Longitudinal Machine Learning Model for Predicting Systolic Blood Pressure in Patients with Heart Failure

Objective: Systolic blood pressure (SBP) is a powerful prognostic factor in heart failure (HF) patients, which is associated with death and readmission. Therefore, control of blood pressure is an important element for managing these patients. The goal of this study was to compare the performance of classical and machine learning models for predicting SBP and identify important variables related to SBP changes over time. Methods: The information of 483 HF patients was analyzed in this retrospective cohort study. These patients were hospitalized at least twice in Farshchian Heart Center Hamadan province, the west of Iran, between October 2015 and July 2019. We applied a linear mixed-effects model (LMM) and mixed-effects least-square support vector regression (MLS-SVR) for predicting SBP. The performance of both models was assessed by mean absolute error, and root mean squared error. Results: Based on LMM results, there was a significant association between sex, body mass index (BMI), sodium, time, and history of hypertension with SBP changes over time (P-value <0.05). Also, MLS-SVR indicated that the four most important variables were history of hypertension, sodium, BMI, and triglyceride. The performance of MLS-SVR compared to LMM was better in both training and testing datasets. Conclusions: According to our results, BMI, sodium, and history of hypertension were the important variables on SBP changes in both LMM and MLS-SVR models. Also, it seems that MLS-SVR can be used as an alternative for classical longitudinal models for predicting SBP in HF patients

Efficiency evaluation of AspergillusTerreus in the removal of chromium (VI) from aqueous solutions:Isotherm and kinetic studies

Background and Aims: Chromium (VI) is one of the very toxic heavy metals and is known as a carcinogenic, mutagenic and teratogenic agent. In this study, the ability of dead Aspergillus Terreus fungus biomass in the removal of chromium (VI) from aqueous solutions was investigated.Materials and Methods: The suspension of AspergillusTerreus was cultivated in Potato Dextrose Agar and Potato Dextrose Broth mediums. The biomass was then boiled in 0.5 N NaOH solution. The ability of obtained biomass to absorb Chromium (VI) was studied with respect to various variables including time (15 to 120 min), pH (3 to 11), chromium (VI) concentration (20 to 120 mg/L) and absorbent dosage (0.1 to 0.8 g). Chromium concentration was determined using an atomic absorption of. All ethical issues and citations were taken into consideration in conducting the study.Results: Results showed that the maximum removal of chromium (89%) was obtained at contact time 90 min, pH=7, chromium concentration 20 mg/L and adsorbent dosage 0.6 g. The adsorption isotherm was best fitted by Freundlich with a high correlation coefficient (R2=0.952). Furthermore, the adsorption kinetics fitted well to the pseudo-first-order model with a correlation coefficient of 0.9775.Conclusion: The results of present study indicated that the studied variables have an incredible effect on sorption efficiency, as in the optimum condition, the biomass of AspergillusTerreus obtained an acceptable efficiency and adoration capacity compared to other adsorbents. So, this compound can be introduced as a practical natural adsorbent for chromium removal and also other heavy metals form aqueous solutions.Keywords: Aqueous solutions, AspergillusTerreus, Chromium (VI), Adsorption Proces

Prediction the survival of patients with breast cancer using random survival forests for competing risks

Abstract Objectives: Breast cancer (BC) is the most common cause of cancer death in Iranian women. Sometimes death from other causes precludes the event of interest and makes the analysis complicated. The purpose of this study was to identify important prognosis factors associated with survival duration among patients with BC using random survival forests (RSF) model in presence of competing risks. Also, its performance was compared with cause-specific hazard model. Methods: This retrospective cohort study assessed 222 patients with BC who admitted in Ayatollah Khansari hospital, Arak. The cause-specific Cox proportional hazards and RSF models were employed to determine the important risk factors for survival of the patients. Results: The mean and median survival duration of the patients were 90.71 (95%CI: 83.8- 97.6) and 100.73 (95%CI: 89.2-- 121.5) months, respectively. The cause-specific model indicated that type of surgery and HER2 had statistically significant effects on the risk of death of BC. Moreover, the RSF model identified that HER2 was the most important variable for the event of interest. Conclusion: According to the results of this study, the performance of the RSF model was better than the cause-specific hazard model. However, HER2 was the most important variable for death of BC in both of the models

Predictors of colorectal cancer screening intention among Iranian adults: an application of the preventive health model

Objective: Colorectal cancer is the third most common cancer among adults in Iran. Colorectal cancer screening is the effective way in reducing deaths from this cancer. However, the screening rate of colorectal cancer is very low among Iranian adults. This study investigated predictors of Iranian average-risk adults ' intention to take up colorectal cancer screening with fecal occult blood test using a mediator model.Methods: Participants of this cross-sectional study were comprised of 478 average-risk adults who were selected using a national sampling frame in Hamadan city, west of Iran. The data gathering instrument was a questionnaire based on the preventive health model constructs. Structural equation modeling (SEM) was employed to test the relationship using Smart PLS 2.0 softwareResults: All measures were robust in terms of its reliability and validity. Benefit (b= 0.12, p<0.01), self efficacy (b= 0.36, p<0.01), social support (b= 0.12, p<.05) and barriers (b= -0.14, p<.01) predicted the intention to be screened for CRC. Self efficacy partly mediated effects of social support and perceived barriers on intention. The study model explained approximately 24% of the variance in CRC screening intention with fecal occult blood testConclusion: Our findings indicated that the preventive health model constructs such as self efficacy, social support and barriers are useful in understanding CRC screening intentions and can help health planners in developing effective interventions for encouraging Iranian adults to undergo CRC screening