Background: Breast cancer (BC) is one of the most common malignancies in women. Early diagnosis of BC and metastasis among the patients based on an accurate system can increase survival of the patients to > 86%. This study aimed to compare the performance of six machine learning techniques two traditional methods for the prediction of BC survival and metastasis. Methods: We used a dataset that include the records of 550 breast cancer patients. Naive Bayes (NB), Random Forest (RF), AdaBoost, Support Vector Machine (SVM), Least-square SVM (LSSVM) and Adabag, Logistic Regression (LR) and Linear Discriminant Analysis were used for the prediction of breast cancer survival and metastasis. The performance of the used techniques was evaluated with sensitivity, specificity, likelihood ratio and total accuracy. Results: Out of 550 patients, 83.4% were alive and 85% did not experience metastasis. In prediction of survival, the average specificity of all techniques was ≥94% and the SVM and LDA have greater sensitivity (73%) in comparison to other techniques. The greater total accuracy (93%) belonged to the SVM and LDA. For metastasis prediction, the RF had the highest specificity (98%), the NB had highest sensitivity (36%) and the LR and LDA had the highest total accuracy (86%). Conclusions: Our finding showed that the SVM outperformed other machine learning methods in prediction of survival of the patients in terms of several criteria. Nevertheless, the LDA technique as a classical method showed similar performance.

Objective Forecasting the time of future outbreaks would minimize the impact of diseases by taking preventive steps including public health messaging and raising awareness of clinicians for timely treatment and diagnosis. The present study investigated the accuracy of support vector machine, artificial neural-network, and random-forest time series models in influenza like illness (ILI) modeling and outbreaks detection. The models were applied to a data set of weekly ILI frequencies in Iran. The root mean square errors (RMSE), mean absolute errors (MAE), and intra-class correlation coefficient (ICC) statistics were employed as evaluation criteria. Results It was indicated that the random-forest time series model outperformed other three methods in modeling weekly ILI frequencies (RMSE = 22.78, MAE = 14.99 and ICC = 0.88 for the test set). In addition neural-network was better in outbreaks detection with total accuracy of 0.889 for the test set. The results showed that the used time series models had promising performances suggesting they could be effectively applied for predicting weekly ILI frequencies and outbreaks. Electronic supplementary material The online version of this article (10.1186/s13104-019-4393-y) contains supplementary material, which is available to authorized users.

ObjectivesDiabetes is one of the most common non-communicable diseases in developing countries. Early screening and diagnosis play an important role in effective prevention strategies. This study compared two traditional classification methods (logistic regression and Fisher linear discriminant analysis) and four machine-learning classifiers (neural networks, support vector machines, fuzzy c-mean, and random forests) to classify persons with and without diabetes.MethodsThe data set used in this study included 6,500 subjects from the Iranian national non-communicable diseases risk factors surveillance obtained through a cross-sectional survey. The obtained sample was based on cluster sampling of the Iran population which was conducted in 2005-2009 to assess the prevalence of major non-communicable disease risk factors. Ten risk factors that are commonly associated with diabetes were selected to compare the performance of six classifiers in terms of sensitivity, specificity, total accuracy, and area under the receiver operating characteristic (ROC) curve criteria.ResultsSupport vector machines showed the highest total accuracy (0.986) as well as area under the ROC (0.979). Also, this method showed high specificity (1.000) and sensitivity (0.820). All other methods produced total accuracy of more than 85%, but for all methods, the sensitivity values were very low (less than 0.350).ConclusionsThe results of this study indicate that, in terms of sensitivity, specificity, and overall classification accuracy, the support vector machine model ranks first among all the classifiers tested in the prediction of diabetes. Therefore, this approach is a promising classifier for predicting diabetes, and it should be further investigated for the prediction of other diseases.

No abstract

No abstract

Introduction Emergency medical services (EMS) providers are at continuous exposure to occupational stressors which negatively affect their health and professional practice. This study explored perceived occupational stressors among EMS providers. Methods This qualitative study was conducted from December 2019 to April 2020 using conventional content analysis. Sixteen EMS providers were purposively selected from EMS stations in Hamadan, Iran. Semi-structured interviews (with length of 45–60 min) were held for data collection. Data were analyzed through Graneheim and Lundman’s conventional content analysis approach. Findings Data analysis resulted in the development of two themes, namely critical conditions of EMS provision, and personal and professional conflicts. The six categories of these two themes were complexity of patients’ clinical conditions, interruption of EMS provision, health hazards, interpersonal problems, interprofessional interactions, and legal conflicts. Conclusion Besides the stress associated with emergency patient care, EMS providers face many different occupational stressors. EMS managers can use the findings of the present study to develop strategies for reducing occupational stress among EMS providers and thereby, improve their health and care quality.

Background: This study aimed to investigate the effect size (ES) of air temperature on the executive functions of human brain and body physiological responses.Methods: In this empirical study, the participants included 35 male students who were exposed to 4 air temperature conditions of 18°C, 22°C, 26°C and 30°C in 4 separate sessions in an air conditioning chamber. The participants were simultaneously asked to take part in the N-back test. The accuracy, electrocardiogram (ECG) signals and the respiration rate were recorded to determine the effect of air temperature. Results: Compared to moderate air temperatures (22°C), high (30°C) and low (18°C) air temperatures had a much more profound effect on changes in heart beat rate, the accuracy of brain executive functions and the response time to stimuli. There were statistically significant differences in the accuracy by different workload levels and various air temperature conditions(P<0.05). Although the heart beat rate index, the ratio between low frequency and high frequency (LF/HF), and the respiratory rate were more profoundly affected by the higher and lower air temperatures than moderate air temperatures (P<0.05), this effect was not statistically significant, which may be due to significant reduction in the standard deviation of normal-to normal intervals (SNND) and the root of mean squared difference between adjacent normal heart beat (N-N) intervals (RMSSD) (P>0.05). Conclusion: The results confirmed that the unfavorable air temperatures may considerably affect the physiological responses and the cognitive functions among indoor employees.Therefore, providing them with thermal comfort may improve their performance within indoor environments.

Background: There has remained a need to better understanding of prognostic factors that affect the survival or risk in patients with human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS), particularly in developing countries. The aim of the present study aimed to identify the prognostic factors influencing AIDS progression in HIV positive patients in Hamadan province of Iran, using random survival forest in the presence of competing risks (death from causes not related to AIDS). This method considers all interactions between variables and their nonlinear effects. Methods: A data set of 585 HIV-infected patients extracted from 1997 to 2011 was utilized. The effect of several prognostic factors on cumulative incidence function (probability) of AIDS progression and death were investigated. Results: The used model indicated that using antiretroviral therapy tuberculosis co-infection are two top most important variables in predicting cumulative incidence function for AIDS progression in the presence of competing risks, respectively. The patients with tuberculosis had much higher predicted cumulative incidence probability. Predicted cumulative incidence probability of AIDS progression was also higher for mother to child mode of HIV transmission. Moreover, transmission type and gender were two top most important variables for the competing event. Men and those patients with IDUS transmission mode had higher predicted risk compared to others. Conclusions: Considering nonlinear effects and interaction between variables, confection with tuberculosis was the most important variable in prediction of cumulative incidence probability of AIDS progression.