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. 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.

Tapak L, et al. Healthc Inform Res. 2017 Oct;23(4):277-284. https://doi.org/10.4258/hir.2017.23.4.277

Payam Amini, Saman Maroufizadeh, Reza Omani Samani, Omid Hamidi, and Mahdi Sepidarkish. Osong Public Health and Research Perspectives 2017;8:195-200. https://doi.org/10.24171/j.phrp.2017.8.3.06

Introduction: The determination of identity factors such as age and sex has gained significance in both criminal and civil cases. Paranasal sinuses like frontal and maxillary sinuses, are resistant to trauma and can aid profiling. We developed a deep learning (DL) model optimized by an evolutionary algorithm (genetic algorithm/GA) to determine sex and age using paranasal sinus parameters based on cone-beam computed tomography (CBCT). Methods: Two hundred and forty CBCT images (including 129 females and 111 males, aged 18–52) were included in this study. CBCT images were captured using the Newtom3G device with specific exposure parameters. These images were then analyzed in ITK-SNAP 3.6.0 beta software to extract four paranasal sinus parameters: height, width, length, and volume for both the frontal and maxillary sinuses. A hybrid model, Genetic Algorithm-Deep Neural Network (GADNN), was proposed for feature selection and classification. Traditional statistical methods and machine learning models, including logistic regression (LR), random forest (RF), multilayer perceptron neural network (MLP), and deep learning (DL) were evaluated for their performance. The synthetic minority oversampling technique was used to deal with the unbalanced data. Results: GADNN showed superior accuracy in both sex determination (accuracy of 86%) and age determination (accuracy of 68%), outperforming other models. Also, DL and RF were the second and third superior methods in sex determination (accuracy of 78% and 71% respectively) and age determination (accuracy of 92% and 57%). Conclusions: The study introduces a novel approach combining DL and GA to enhance sex determination and age determination accuracy. The potential of DL in forensic dentistry is highlighted, demonstrating its efficiency in improving accuracy for sex determination and age determination. The study contributes to the burgeoning field of DL in dentistry and forensic sciences.

The large-scale variability of atmospheric and ocean circulation patterns cause seasonal climate changes in the Earth. In other words, climate elements are affected by phenomena like El Niño Southern Oscillation (ENSO), El Niño (NINO), and Northern Atlantic Oscillation (NAO). In this study the characteristics of the frost season over a 20-year period (1996–2015) from seven synoptic stations in western Iran were evaluated using support vector machine and random forest regression. Comparing determination coefficients obtained by these models between atmospheric and ocean circulation indices and the characteristics of the frost season showed a positive effect. Thus, the onset and the end of the frost season in this region were highly correlated with the Southern Oscillation Index (SOI) and NAO, respectively. In regions with lower correlations (central areas and some regions of Alvand Mountain), the role of the geographical factors, altitude and topography becomes more pronounced and the impact of the global indices is reduced. Cluster analysis was also conducted to detect patterns and to identify regions according to the effect of the atmospheric and oceanic indices on frost season, and three regions were identified. The largest correlations with global indices (in both models) belonged to the first and third classes, respectively. The results of this study could be applied for planning environmental and agricultural activities.