Tracking and analysis of discourse dynamics and polarity during the early Corona pandemic in Iran.

It has not been long since a new disease called COVID-19 has hit the international community. Unknown nature of the virus, evidence of its adaptability and survival in new conditions, its widespread prevalence and also lengthy recovery period, along with daily notifications of new infection and fatality statistics, have created a wave of fear and anxiety among the public community and authorities. These factors have led to extreme changes in the social discourse in a rather short period of time. The analysis of this discourse is important to reconcile the society and restore ordinary conditions of mental peace and health. Although much research has been done on the disease since its international pandemic, the sociological analysis of the recent public phenomenon, especially in developing countries, still needs attention. We propose a framework for analyzing social media data and news stories oriented around COVID-19 disease. Our research is based on an extensive Persian data set gathered from different social media networks and news agencies in the period of January 21-April 29, 2020. We use the Latent Dirichlet Allocation (LDA) model and dynamic topic modeling to understand and capture the change of discourse in terms of temporal subjects. We scrutinize the reasons of subject alternations by exploring the related events and adopted practices and policies. The social discourse can highly affect the community morale and polarization. Therefore, we further analyze the polarization in online social media posts, and detect points of concept drift in the stream. Based on the analyzed content, effective guidelines are extracted to shift polarization towards positive. The results show that the proposed framework is able to provide an effective practical approach for cause and effect analysis of the social discourse.

Survival Prediction and Feature Selection in Patients with Breast Cancer Using Support Vector Regression.

The Support Vector Regression (SVR) model has been broadly used for response prediction. However, few researchers have used SVR for survival analysis. In this study, a new SVR model is proposed and SVR with different kernels and the traditional Cox model are trained. The models are compared based on different performance measures. We also select the best subset of features using three feature selection methods: combination of SVR and statistical tests, univariate feature selection based on concordance index, and recursive feature elimination. The evaluations are performed using available medical datasets and also a Breast Cancer (BC) dataset consisting of 573 patients who visited the Oncology Clinic of Hamadan province in Iran. Results show that, for the BC dataset, survival time can be predicted more accurately by linear SVR than nonlinear SVR. Based on the three feature selection methods, metastasis status, progesterone receptor status, and human epidermal growth factor receptor 2 status are the best features associated to survival. Also, according to the obtained results, performance of linear and nonlinear kernels is comparable. The proposed SVR model performs similar to or slightly better than other models. Also, SVR performs similar to or better than Cox when all features are included in model.

A data mining approach for diagnosis of coronary artery disease.

Cardiovascular diseases are very common and are one of the main reasons of death. Being among the major types of these diseases, correct and in-time diagnosis of coronary artery disease (CAD) is very important. Angiography is the most accurate CAD diagnosis method; however, it has many side effects and is costly. Existing studies have used several features in collecting data from patients, while applying different data mining algorithms to achieve methods with high accuracy and less side effects and costs. In this paper, a dataset called Z-Alizadeh Sani with 303 patients and 54 features, is introduced which utilizes several effective features. Also, a feature creation method is proposed to enrich the dataset. Then Information Gain and confidence were used to determine the effectiveness of features on CAD. Typical Chest Pain, Region RWMA2, and age were the most effective ones besides the created features by means of Information Gain. Moreover Q Wave and ST Elevation had the highest confidence. Using data mining methods and the feature creation algorithm, 94.08% accuracy is achieved, which is higher than the known approaches in the literature.

Diagnosing Coronary Artery Disease via Data Mining Algorithms by Considering Laboratory and Echocardiography Features.

BACKGROUND: Coronary artery disease (CAD) is the result of the accumulation of athermanous plaques within the walls of coronary arteries, which supply the myocardium with oxygen and nutrients. CAD leads to heart attacks or strokes and is, thus, one of the most important causes of death worldwide. Angiography, an imaging modality for blood vessels, is currently the most accurate method of diagnosing artery stenosis. However, the disadvantages of this method such as complications, costs, and possible side effects have prompted researchers to investigate alternative solutions. OBJECTIVES: The current study aimed to use data analysis, a non-invasive and less costly method, and various data mining algorithms to predict the stenosis of arteries. Among many people who refer to hospitals due to chest pain, a great number of them are normal and as such do not need angiography. The objective of this study was to predict patients who are most probably normal using features with the highest correlations with CAD with a view to obviate angiography costs and complications. Not a substitute for angiography, this method would select high-risk cases that definitely need angiography. PATIENTS AND METHODS: Different features were measured and collected from potential patients in order to construct a dataset, which was later utilized for model extraction. Most of the proposed methods in the literature have not considered the stenosis of each artery separately, whereas the present study employed laboratory and echocardiographic data to diagnose the stenosis of each artery separately. The data were gathered from 303 random visitors to Rajaie Cardiovascular, Medical and Research Center. Electrocardiographic (ECG) data were studied in our previous works. The goal of this study was, therefore, to seek the accuracy of echocardiographic and laboratory features to predict CAD patients that require angiography. RESULTS: Bagging and C4.5 classification algorithms were drawn upon to analyse the data, the former reaching accuracy rates of 79.54%, 61.46%, and 68.96% for the diagnosis of the stenoses of the left anterior descending (LAD), left circumflex (LCX), and right coronary artery (RCA), respectively. The accuracy to predict the LAD stenosis was attained via feature selection. In the current study, features effective in the stenosis of arteries were further determined, and some rules for the evaluation of triglyceride, hemoglobin, hypertension, dyslipidemia, diabetes mellitus, and ejection fraction were extracted. CONCLUSIONS: The current study presents the highest accuracy value to diagnose the LAD stenosis in the literature.

Diagnosis of coronary arteries stenosis using data mining.

Cardiovascular diseases are one of the most common diseases that cause a large number of deaths each year. Coronary Artery Disease (CAD) is the most common type of these diseases worldwide and is the main reason of heart attacks. Thus early diagnosis of CAD is very essential and is an important field of medical studies. Many methods are used to diagnose CAD so far. These methods reduce cost and deaths. But a few studies examined stenosis of each vessel separately. Determination of stenosed coronary artery when significant ECG abnormality exists is not a difficult task. Moreover, ECG abnormality is not common among CAD patients. The aim of this study is to find a way for specifying the lesioned vessel when there is not enough ECG changes and only based on risk factors, physical examination and Para clinic data. Therefore, a new data set was used which has no missing value and includes new and effective features like Function Class, Dyspnoea, Q Wave, ST Elevation, ST Depression and Tinversion. These data was collected from 303 random visitor of Tehran's Shaheed Rajaei Cardiovascular, Medical and Research Centre, in 2011 fall and 2012 winter. They processed with C4.5, Naïve Bayes, and k-nearest neighbour (KNN) algorithms and their accuracy were measured by tenfold cross validation. In the best method the accuracy of diagnosis of stenosis of each vessel reached to 74.20 ± 5.51% for Left Anterior Descending (LAD), 63.76 ± 9.73% for Left Circumflex and 68.33 ± 6.90% for Right Coronary Artery. The effective features of stenosis of each vessel were found too.