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.

Background: The importance of sleep in physical and mental well-being is generally acknowledged by both health professionals and the general public.This study investigated the effects on inhalation aromatherapy on sleep quality in cardiac patients.Methods: Ninety-six men and women aged between 20 and 75 were randomized to inhale aromatherapy essential oil from Lemon Balm (Melissa officinalis L.) or odorless sesame oil (the placebo) for 30 minutes twice daily for 3 days. Sleep quality by Verran Synder- Halpern (VSH) scale were assessed before and after period.Results: Compared with the placebo, the experimental group showed that the components such as subjective sleep quality, sleep disturbance, and daytime dysfunction were significantly decreased (P< 0.005).Conclusion: Aromatherapy may be used as an independent nursing intervention for improving sleep quality of cardiac patients.

Background Late-diagnosis of HIV is a major challenge for the control and prevention of AIDS in the world. The present study aimed to specify factors associated with the late diagnosis of HIV in Iran from 1987 to 2016. Methods In this retrospective cohort study, data for 4402 diagnosed HIV/AIDS patients were extracted from 158 behavioral disease counseling centers of 31 Iranian provinces. We defined late diagnosis as having a CD4 count less than 350 within 3 months after diagnosis. Multiple logistic regression analysis was used to determine the factors influencing late diagnosis. Moreover, we used multivariate Cox regression to assess the association of these factors with the patients’ survival. Results In this study, the prevalence of late diagnosis among the patients was 58.2%. People aged 50 years and over (adjusted OR = 3.55), transmission through blood transfusion (adjusted OR = 2.89), co-infection with tuberculosis (adjusted OR = 2.06), and male gender (adjusted OR = 1.38) were the strongest predictors for late diagnosis of HIV. On the other hand, baseline CD4 (adjusted HR = 2.21), people aged 50 and over (adjusted HR = 1.81), male gender (adjusted HR = 1.76), being a widow (adjusted HR = 1.68), people with unknown transmission way (adjusted HR = 18.24), people who inject drugs (adjusted HR = 1.87), diagnosis at previous years (adjusted HR = 2.45) and co-infection with tuberculosis (adjusted OR = 1.77) significantly associated with the survival of patients. Conclusion The prevalence of late diagnosis is high among Iranian HIV/AIDS. The risk factors of late diagnoses include being males and aged 50 years and over, transmission through blood transfusion, and co-infection with tuberculosis. Therefore, implementation of screening programs for early diagnosis of HIV these high risk groups is recommended to Iranian health providers and policymakers.

The early and accurately detection of brucellosis incidence change is of great importance for implementing brucellosis prevention strategic health planning. The present study investigated and compared the performance of the three data mining techniques, random forest (RF), support vector machine (SVM) and multivariate adaptive regression splines (MARSs), in time series modelling and predicting of monthly brucellosis data from 2005 (March/April) to 2017 (February/March) extracted from a national public health surveillance system in Hamadan located in west of Iran. The performances were compared based on the root mean square errors, mean absolute errors, determination coefficient (R2) and intraclass correlation coefficient criteria. Results indicated that the RF model outperformed the SVM and MARS models in modeling used data and it can be utilized successfully utilized to diagnose the behaviour of brucellosis over time. Further research with application of such novel time series models in practice provides the most appropriate method in the control and prevention of future outbreaks for the epidemiologist.

Objective CD4 Lymphocyte Count (CD4) is a major predictor of HIV progression to AIDS. Exploring the factors affecting CD4 levels may assist healthcare staff and patients in management and monitoring of health cares. This retrospective cohort study aimed to explore factors associated with CD4 cell counts at the time of diagnosis in HIV patients using Poisson, Generalized Poisson, and Negative Binomial regression models. Results Out of 4402 HIV patients diagnosis in Iran from 1987 to 2016, 3030 (68.8%) were males, and the mean age was 34.8 ± 10.4 years. The results indicate that the Negative Binomial model outperformed the other models in terms of AIC, log-likelihood and RMSE criteria. In this model, factors include sex, age, clinical stage and Tuberculosis (TB) co-infection were significantly associated with CD4 count (P < 0.05). Conclusion Given the effect of age, sex, clinical stage and stage of HIV on CD4 count of the patients, adopting policies and strategies to increase awareness and encourage people to seek early HIV testing and care is advantageous.