Artificial neural network and logistic regression modelling to characterize COVID-19 infected patients in local areas of Iran

The relationship between anthropometric indices and the presence of hypertension in an Iranian population sample using data mining algorithms

Hypertension (HTN) is a common chronic condition associated with increased morbidity and mortality. Anthropometric indices of adiposity are known to be associated with a risk of HTN. The aim of this study was to identify the anthropometric indices that best associate with HTN in an Iranian population. 9704 individuals aged 35-65 years were recruited as part of the Mashhad Stroke and Heart Atherosclerotic Disorder (MASHAD) study. Demographic and anthropometric data of all participants were recorded. HTN was defined as a systolic blood pressure (SBP) ≥ 140 mmHg, and/ or a diastolic blood pressure (DBP) ≥ 90 mmHg on two subsequent measurements, or being treated with oral drug therapy for BP. Data mining methods including Logistic Regression (LR), Decision Tree (DT), and Bootstrap Forest (BF) were applied. Of 9704 participants, 3070 had HTN, and 6634 were normotensive. LR showed that body roundness index (BRI), body mass index (BMI) and visceral adiposity index (VAI) were significantly associated with HTN in both genders (P < 0.0001). BRI showed the greatest association with HTN (OR = 1.276, 95%CI = (1.224, 1.330)). For BMI we had OR = 1.063, 95%CI = (1.047, 1.080), for VAI we had OR = 1.029, 95%CI = (1.020, 1.038). An age < 47 years and BRI < 4.04 was associated with a 90% probability of being normotensive. The BF indicated that age, sex and BRI had the most important role in HTN. In summary, among anthropometric indices the most powerful indicator for discriminating hypertensive from normotensive patients was BRI.

White blood cell and platelet distribution widths are associated with hypertension: data mining approaches

In this paper, we are going to investigate the association between Hypertension (HTN) and routine hematologic indices in a cohort of Iranian adults. The data were obtained from a total population of 9704 who were aged 35-65 years, a prospective study was designed. The association between hematologic factors and HTN was assessed using logistic regression (LR) analysis and a decision tree (DT) algorithm. A total of 9704 complete datasets were analyzed in this cohort study (N = 3070 with HTN [female 62.47% and male 37.52%], N = 6634 without HTN [female 58.90% and male 41.09%]). Several variables were significantly different between the two groups, including age, smoking status, BMI, diabetes millitus, high sensitivity C-reactive protein (hs-CRP), uric acid, FBS, total cholesterol, HGB, LYM, WBC, PDW, RDW, RBC, sex, PLT, MCV, SBP, DBP, BUN, and HCT (P < 0.05). For unit odds ratio (OR) interpretation, females are more likely to have HTN (OR = 1.837, 95% CI = (1.620, 2.081)). Among the analyzed variables, age and WBC had the most significant associations with HTN OR = 1.087, 95% CI = (1.081, 1.094) and OR = 1.096, 95% CI = (1.061, 1.133), respectively (P-value < 0.05). In the DT model, age, followed by WBC, sex, and PDW, has the most significant impact on the HTN risk. Ninety-eight percent of patients had HTN in the subgroup with older age (≥58), high PDW (≥17.3), and low RDW (<46). Finally, we found that elevated WBC and PDW are the most associated factor with the severity of HTN in the Mashhad general population as well as female gender and older age.

Prediction of atmospheric PM2.5 level by machine learning techniques in Isfahan, Iran

With increasing levels of air pollution, air quality prediction has attracted more attention. Mathematical models are being developed by researchers to achieve precise predictions. Monitoring and prediction of atmospheric PM2.5 levels, as a predominant pollutant, is essential in emission mitigation programs. In this study, meteorological datasets from 9 years in Isfahan city, a large metropolis of Iran, were applied to predict the PM2.5 levels, using four machine learning algorithms including Artificial Neural |Networks (ANNs), K-Nearest-Neighbors (KNN), Support Vector |Machines (SVMs) and ensembles of classification trees Random Forest (RF). The data from 7 air quality monitoring stations located in Isfahan City were taken into consideration. The Confusion Matrix and Cross-Entropy Loss were used to analyze the performance of classification models. Several parameters, including sensitivity, specificity, accuracy, F1 score, precision, and the area under the curve (AUC), are computed to assess model performance. Finally, by introducing the predicted data for 2020 into ArcGIS software and using the IDW (Inverse Distance Weighting) method, interpolation was conducted for the area of Isfahan city and the pollution map was illustrated for each month of the year. The results showed that, based on the accuracy percentage, the ANN model has a better performance (90.1%) in predicting PM2.5 grades compared to the other models for the applied meteorological dataset, followed by RF (86.1%), SVM (84.6%) and KNN (82.2%) models, respectively. Therefore, ANN modelling provides a feasible procedure for the managerial planning of air pollution control.

Association between biochemical and hematologic factors with COVID-19 using data mining methods

BACKGROUND AND AIM

Coronavirus disease (COVID-19) is an infectious disease that can spread very rapidly with important public health impacts. The prediction of the important factors related to the patient's infectious diseases is helpful to health care workers. The aim of this research was to select the critical feature of the relationship between demographic, biochemical, and hematological characteristics, in patients with and without COVID-19 infection.

METHOD

A total of 13,170 participants in the age range of 35-65 years were recruited. Decision Tree (DT), Logistic Regression (LR), and Bootstrap Forest (BF) techniques were fitted into data. Three models were considered in this study, in model I, the biochemical features, in model II, the hematological features, and in model II, both biochemical and homological features were studied.

RESULTS

In Model I, the BF, DT, and LR algorithms identified creatine phosphokinase (CPK), blood urea nitrogen (BUN), fasting blood glucose (FBG), total bilirubin, body mass index (BMI), sex, and age, as important predictors for COVID-19. In Model II, our BF, DT, and LR algorithms identified BMI, sex, mean platelet volume (MPV), and age as important predictors. In Model III, our BF, DT, and LR algorithms identified CPK, BMI, MPV, BUN, FBG, sex, creatinine (Cr), age, and total bilirubin as important predictors.

CONCLUSION

The proposed BF, DT, and LR models appear to be able to predict and classify infected and non-infected people based on CPK, BUN, BMI, MPV, FBG, Sex, Cr, and Age which had a high association with COVID-19.

Platelet distribution widths and white blood cell are associated with cardiovascular diseases: data mining approaches

OBJECTIVE

To investigate the association between cardiovascular diseases (CVDs) and haematologic factors in a cohort of Iranian adults.

METHOD

For a total population of 9,704 aged 35 to 65, a prospective study was designed. Haematologic factors and demographic characteristics (such as gender, age, and smoking status) were completed for all participants. The association between haematologic factors and CVDs was assessed through logistic regression (LR) analysis, decision tree (DT), and bootstrap forest (BF).

RESULTS

Almost all of the included factors were significantly associated with CVD (p<.001). Among the included factors, were: age, white blood cell (WBC), and platelet distribution width (PDW) had the strongest correlation with the development of CVD. For unit OR interpretation, WBC has been represented as the most remarkable risk factor for CVD (OR: 1.22 (CI 95% (1.18, 1.27))). Also, age is associated with an increase in the odds of CVD + occurrence (OR: 1.12 (CI 95% (1.11, 1.13))). Moreover, males are times more likely to develop CVD than females (OR: 1.39 (CI 95% (1.22, 1.58))). In DT model, age is the best classifier factor in CVD development, followed by WBC and PDW. Furthermore, based on the BF algorithm, the most crucial factors correlated with CVD are age, WBC, PDW, sex, and smoking status.

CONCLUSION

The obtained result from LR, DT, and BF models confirmed that age, WBC, and PDW are the most crucial factors for the development of CVD.

Use of data mining approaches to explore the association between type 2 diabetes mellitus with SARS-CoV-2

BACKGROUND AND OBJECTIVE

Corona virus causes respiratory tract infections in mammals. The latest type of Severe Acute Respiratory Syndrome Corona-viruses 2 (SARS-CoV-2), Corona virus spread in humans in December 2019 in Wuhan, China. The purpose of this study was to investigate the relationship between type 2 diabetes mellitus (T2DM), and their biochemical and hematological factors with the level of infection with COVID-19 to improve the treatment and management of the disease.

MATERIAL AND METHOD

This study was conducted on a population of 13,170 including 5780 subjects with SARS-COV-2 and 7390 subjects without SARS-COV-2, in the age range of 35-65 years. Also, the associations between biochemical factors, hematological factors, physical activity level (PAL), age, sex, and smoking status were investigated with the COVID-19 infection.

RESULT

Data mining techniques such as logistic regression (LR) and decision tree (DT) algorithms were used to analyze the data. The results using the LR model showed that in biochemical factors (Model I) creatine phosphokinase (CPK) (OR: 1.006 CI 95% (1.006,1.007)), blood urea nitrogen (BUN) (OR: 1.039 CI 95% (1.033, 1.047)) and in hematological factors (Model II) mean platelet volume (MVP) (OR: 1.546 CI 95% (1.470, 1.628)) were significant factors associated with COVID-19 infection. Using the DT model, CPK, BUN, and MPV were the most important variables. Also, after adjustment for confounding factors, subjects with T2DM had higher risk for COVID-19 infection.

CONCLUSION

There was a significant association between CPK, BUN, MPV and T2DM with COVID-19 infection and T2DM appears to be important in the development of COVID-19 infection.

Development of Data Mining Algorithms for Identifying the Best Anthropometric Predictors for Cardiovascular Disease: MASHAD Cohort Study

INTRODUCTION

Many studies have been published to assess the best anthropometric measurements associated with cardiovascular diseases (CVDs), but controversies still exist.

AIM

Investigating the association between CVDs and anthropometric measurements among Iranian adults.

METHODS

For a total population of 9354 aged 35 to 65, a prospective study was designed. Anthropometric measurements including ABSI (A Body Shape Index), Body Adiposity Index (BAI), Body Mass Index (BMI), Waist to Height Ratio (WHtR), Body Round Index (BRI), HC (Hip Circumference), Demispan, Mid-arm circumference (MAC), Waist-to-hip (WH) and Waist Circumference (WC) were completed. The association between these parameters and CVDs were assessed through logistic regression (LR) and decision tree (DT) models.

RESULTS

During the 6-year follow-up, 4596 individuals (49%) developed CVDs. According to the LR, age, BAI, BMI, Demispan, and BRI, in male and age, WC, BMI, and BAI in female had a significant association with CVDs (p-value < 0.03). Age and BRI for male and age and BMI for female represent the most appropriate estimates for CVDs (OR: 1.07, (95% CI: 1.06, 1.08), 1.36 (1.22, 1.51), 1.14 (1.13, 1.15), and 1.05 (1.02, 1.07), respectively). In the DT for male, those with BRI ≥ 3.87, age ≥ 46 years, and BMI ≥ 35.97 had the highest risk to develop CVDs (90%). Also, in the DT for female, those with age ≥ 54 years and WC ≥ 84 had the highest risk to develop CVDs (71%).

CONCLUSION

BRI and age in male and age and BMI in female had the greatest association with CVDs. Also, BRI and BMI was the strongest indices for this prediction.

Prediction of type 2 diabetes mellitus using hematological factors based on machine learning approaches: a cohort study analysis

Type 2 Diabetes Mellitus (T2DM) is a significant public health problem globally. The diagnosis and management of diabetes are critical to reduce the diabetes complications including cardiovascular disease and cancer. This study was designed to assess the potential association between T2DM and routinely measured hematological parameters. This study was a subsample of 9000 adults aged 35-65 years recruited as part of Mashhad stroke and heart atherosclerotic disorder (MASHAD) cohort study. Machine learning techniques including logistic regression (LR), decision tree (DT) and bootstrap forest (BF) algorithms were applied to analyze data. All data analyses were performed using SPSS version 22 and SAS JMP Pro version 13 at a significant level of 0.05. Based on the performance indices, the BF model gave high accuracy, precision, specificity, and AUC. Previous studies suggested the positive relationship of triglyceride-glucose (TyG) index with T2DM, so we considered the association of TyG index with hematological factors. We found this association was aligned with their results regarding T2DM, except MCHC. The most effective factors in the BF model were age and WBC (white blood cell). The BF model represented a better performance to predict T2DM. Our model provides valuable information to predict T2DM like age and WBC.

Diagnosis of Coronary Artery Disease based on Machine Learning algorithms Support Vector Machine, Artificial Neural Network, and Random Forest

Background

Coronary artery disease (CAD) is known as the most common cardiovascular disease. The development of CAD is influenced by several risk factors. Diagnostic and therapeutic methods of this disease have many and costly side effects. Therefore, researchers are looking for cost-effective and accurate methods to diagnose this disease. Machine learning algorithms can help specialists diagnose the disease early. The aim of this study is to detect CAD using machine learning algorithms.

Materials and Methods

In this study, three data mining algorithms support vector machine (SVM), artificial neural network (ANN), and random forest were used to predict CAD using the Isfahan Cohort Study dataset of Isfahan Cardiovascular Research Center. 19 features with 11495 records from this dataset were used for this research.

Results

All three algorithms achieved relatively close results. However, the SVM had the highest accuracy compared to the other techniques. The accuracy was calculated as 89.73% for SVM. The ANN algorithm also obtained the high area under the curve, sensitivity and accuracy and provided acceptable performance. Age, sex, Sleep satisfaction, history of stroke, history of palpitations, and history of heart disease were most correlated with target class. Eleven rules were also extracted from this dataset with high confidence and support.

Conclusion

In this study, it was shown that machine learning algorithms can be used with high accuracy to detect CAD. Thus, it allows physicians to perform timely preventive treatment in patients with CAD.

Efficacy of Hydroxychloroquine in Pre-exposure Severe Acute Respiratory Syndrome Coronavirus 2 Prophylaxis among High-Risk HealthCare Workers: A Multicenter Study

Background

Prophylaxis could be an established strategy to potentially prevent and control infectious diseases and should be considered in the coronavirus disease 2019 (COVID-19) pandemic. The present study aimed to assess the effectiveness of hydroxychloroquine as a prophylaxis treatment strategy in the reduction of the risk of COVID-19 among health professionals.

Materials and Methods

The health professionals were randomly assigned (1:1) to the control group without receiving any hydroxychloroquine as prophylaxis and the hydroxychloroquine group receiving a weekly hydroxychloroquine dose of 400 mg up to 12 weeks.

Results

A total of 146 health professionals were randomly enrolled in this study between August 11 and November 11 in 2020. Among the screened health professionals, 21 (14.6%) were infected with COVID-19 during the 12 weeks, and 14 (66.6%) out of the 21 health professionals were in the control group. Most participants with COVID-19 had mild symptoms (62%). In addition, 9.5% (n = 2) of the participants suffered from moderate disease and 28.5% were diagnosed with severe symptoms. In the hydroxychloroquine group, 5 (7.1%) and 2 (2.8%) participants were reported with mild and moderate symptoms of COVID-19, respectively, and 2 participants had moderate, 8 (10.9%) participants had mild symptoms, and 6 (8.2%) participants had severe symptoms in the control group, within 3 months. Severe symptoms of COVID-19 were not observed in the hydroxychloroquine group.

Conclusion

This study addressed the effect and benefit of hydroxychloroquine administration for the prevention of COVID-19 among health professionals. The improved perception of prophylaxis might highlight its important role in future COVID-19 outbreaks to prevent hospital transmission, which is a major route of spread.

Estimation of COVID-19 patient numbers using artificial neural networks based on air pollutant concentration levels

The dilemma between health concerns and the economy is apparent in the context of strategic decision making during the pandemic. In particular, estimating the patient numbers and achieving an informed management of the dilemma are crucial in terms of the strategic decisions to be taken. The Covid-19 pandemic presents an important case in this context. Sustaining the efforts to cope with and to put an end to this pandemic requires investigation of the spread and infection mechanisms of the disease, and the factors which facilitate its spread. Covid-19 symptoms culminating in respiratory failure are known to cause death. Since air quality is one of the most significant factors in the progression of lung and respiratory diseases, it is aimed to estimate the number of Covid-19 patients corresponding to the pollutant parameters (PM10, PM2.5, SO₂, NO_X, NO₂, CO, O₃) after determining the relationship between air pollutant parameters and Covid-19 patient numbers in Turkey. For this purpose, artificial neural network was used to estimate the number of Covid-19 patients corresponding to air pollutant parameters in Turkey. To obtain highest accuracy levels in terms of network architecture structure, various network structures were tested. The optimal performance level was developed with 15 neurons combined with one hidden layer, which achieved a network performance level as high as 0.97342. It was concluded that Covid-19 disease is affected from air pollutant parameters and the number of patients can be estimated depending on these parameters by this study. Since it is known that the struggle against the pandemic should be handled in all aspects, the result of the study will contribute to the establishment of environmental decisions and precautions.

Mapping potential desertification-prone areas in North-Eastern Algeria using logistic regression model, GIS, and remote sensing techniques

Desertification is an environmental threat that affects many countries in the world, and it poses specially an ecological issue to Algeria. This study aimed to assess areas sensitive to desertification in North-Eastern Algeria (Tebessa province) using a logistic regression model (LRM), and geomatics-based approaches. Topsoil Grain Size Index (TGSI), Normalized Difference Vegetation Index (NDVI), Aridity index (AI), and Anthropic pressure on the steppe environment (APSE) were selected as desertification indicators for representing land surface conditions from soil, vegetation, climate, and anthropic disruptors. Results indicate that both AI and TGSI are the most crucial indices conditioning desertification risk. Other indices; NDVI and ASPE were appeared as secondary important indices. Herein, although vegetation generally is a key factor for reading desertification, this result shows that vegetation changes in this study are less important than other desertification conditioning parameters. Area under curve value equal 0.94 indicates a satisfactory accuracy for the proposed model. In total, desertification risk changes increasingly along a North-to-South gradient of the whole research area. Besides, slight, moderate, high, and very high classes occupied 0.87%, 21.08%, 19.33% and 58.72% of the total land area, respectively. LRM is recommended as an accurate and easily applied tool to monitor desertification, especially in scarce data environment in developing countries. Additionally, the results obtained in this paper represent a basic scientific tool for implementing current and future policies to control desertification at areas with high risk.

A conceptual IoT-based early-warning architecture for remote monitoring of COVID-19 patients in wards and at home

Due to the COVID-19 pandemic, health services around the globe are struggling. An effective system for monitoring patients can improve healthcare delivery by avoiding in-person contacts, enabling early-detection of severe cases, and remotely assessing patients' status. Internet of Things (IoT) technologies have been used for monitoring patients' health with wireless wearable sensors in different scenarios and medical conditions, such as noncommunicable and infectious diseases. Combining IoT-related technologies with early-warning scores (EWS) commonly utilized in infirmaries has the potential to enhance health services delivery significantly. Specifically, the NEWS-2 has been showing remarkable results in detecting the health deterioration of COVID-19 patients. Although the literature presents several approaches for remote monitoring, none of these studies proposes a customized, complete, and integrated architecture that uses an effective early-detection mechanism for COVID-19 and that is flexible enough to be used in hospital wards and at home. Therefore, this article's objective is to present a comprehensive IoT-based conceptual architecture that addresses the key requirements of scalability, interoperability, network dynamics, context discovery, reliability, and privacy in the context of remote health monitoring of COVID-19 patients in hospitals and at home. Since remote monitoring of patients at home (essential during a pandemic) can engender trust issues regarding secure and ethical data collection, a consent management module was incorporated into our architecture to provide transparency and ensure data privacy. Further, the article details mechanisms for supporting a configurable and adaptable scoring system embedded in wearable devices to increase usefulness and flexibility for health care professions working with EWS.

The Pandemic's Toll on Young Adolescents: Prevention and Intervention Targets to Preserve Their Mental Health

PURPOSE

Adolescence is characterized by dramatic physical, social, and emotional changes, making teens particularly vulnerable to the mental health effects of the COVID-19 pandemic. This longitudinal study identifies young adolescents who are most vulnerable to the psychological toll of the pandemic and provides insights to inform strategies to help adolescents cope better in times of crisis.

METHODS

A data-driven approach was applied to a longitudinal, demographically diverse cohort of more than 3,000 young adolescents (11-14 years) participating in the ongoing Adolescent Brain Cognitive Development Study in the United States, including multiple prepandemic visits and three assessments during the COVID-19 pandemic (May-August 2020). We fitted machine learning models and provided a comprehensive list of predictors of psychological distress in individuals.

RESULTS

Positive affect, stress, anxiety, and depressive symptoms were accurately detected with our classifiers. Female sex and prepandemic internalizing symptoms and sleep problems were strong predictors of psychological distress. Parent- and youth-reported pandemic-related psychosocial factors, including poorer quality and functioning of family relationships, more screen time, and witnessing discrimination in relation to the pandemic further predicted youth distress. However, better social support, regular physical activities, coping strategies, and healthy behaviors predicted better emotional well-being.

DISCUSSION

Findings highlight the importance of social connectedness and healthy behaviors, such as sleep and physical activity, as buffering factors against the deleterious effects of the pandemic on adolescents' mental health. They also point to the need for greater attention toward coping strategies that help the most vulnerable adolescents, particularly girls and those with prepandemic psychological problems.

Using logistic regression to develop a diagnostic model for COVID-19: A single-center study

BACKGROUND

The main manifestations of coronavirus disease-2019 (COVID-19) are similar to the many other respiratory diseases. In addition, the existence of numerous uncertainties in the prognosis of this condition has multiplied the need to establish a valid and accurate prediction model. This study aimed to develop a diagnostic model based on logistic regression to enhance the diagnostic accuracy of COVID-19.

MATERIALS AND METHODS

A standardized diagnostic model was developed on data of 400 patients who were referred to Ayatollah Talleghani Hospital, Abadan, Iran, for the COVID-19 diagnosis. We used the Chi-square correlation coefficient for feature selection, and logistic regression in SPSS V25 software to model the relationship between each of the clinical features. Potentially diagnostic determinants extracted from the patient's history, physical examination, and laboratory and imaging testing were entered in a logistic regression analysis. The discriminative ability of the model was expressed as sensitivity, specificity, accuracy, and area under the curve, respectively.

RESULTS

After determining the correlation of each diagnostic regressor with COVID-19 using the Chi-square method, the 15 important regressors were obtained at the level of P < 0.05. The experimental results demonstrated that the binary logistic regression model yielded specificity, sensitivity, and accuracy of 97.3%, 98.8%, and 98.2%, respectively.

CONCLUSION

The destructive effects of the COVID-19 outbreak and the shortage of healthcare resources in fighting against this pandemic require increasing attention to using the Clinical Decision Support Systems equipped with supervised learning classification algorithms such as logistic regression.

Application of intelligence-based computational techniques for classification and early differential diagnosis of COVID-19 disease

Clinical methods are used for diagnosing COVID-19 infected patients, but reports posit that, several people who were initially tested positive of COVID-19, and who had some underlying diseases, turned out having negative results after further tests. Therefore, the performance of clinical methods is not always guaranteed. Moreover, chest X-ray image data of COVID-19 infected patients are mostly used in the computational models for COVID-19 diagnosis, while the use of common symptoms, such as fever, cough, fatigue, muscle aches, headache, etc. in computational models is not yet reported. In this study, we employed seven classification algorithms to empirically test and verify their efficacy when applied to diagnose COVID-19 using the aforementioned symptoms. We experimented with Logistic Regression (LR), Support Vector Machine (SVM), Naïve Byes (NB), Decision Tree (DT), Multilayer Perceptron (MLP), Fuzzy Cognitive Map (FCM) and Deep Neural Network (DNN) algorithms. The techniques were subjected to random undersampling and oversampling. Our results showed that with class imbalance, MLP and DNN outperform others. However, without class imbalance, MLP, FCM and DNN outperform others with the use of random undersampling, but DNN has the best performance by utilizing random oversampling. This study identified MLP, FCM and DNN as better classifiers over LR, NB, DT and SVM, so that healthcare software system developers can adopt them to develop intelligence-based expert systems which both medical personnel and patients can use for differential diagnosis of COVID-19 based on the aforementioned symptoms. However, the test of performance must not be limited to the traditional performance metrics.

Boosting COVID-19 Image Classification Using MobileNetV3 and Aquila Optimizer Algorithm

Currently, the world is still facing a COVID-19 (coronavirus disease 2019) classified as a highly infectious disease due to its rapid spreading. The shortage of X-ray machines may lead to critical situations and delay the diagnosis results, increasing the number of deaths. Therefore, the exploitation of deep learning (DL) and optimization algorithms can be advantageous in early diagnosis and COVID-19 detection. In this paper, we propose a framework for COVID-19 images classification using hybridization of DL and swarm-based algorithms. The MobileNetV3 is used as a backbone feature extraction to learn and extract relevant image representations as a DL model. As a swarm-based algorithm, the Aquila Optimizer (Aqu) is used as a feature selector to reduce the dimensionality of the image representations and improve the classification accuracy using only the most essential selected features. To validate the proposed framework, two datasets with X-ray and CT COVID-19 images are used. The obtained results from the experiments show a good performance of the proposed framework in terms of classification accuracy and dimensionality reduction during the feature extraction and selection phases. The Aqu feature selection algorithm achieves accuracy better than other methods in terms of performance metrics.

The most places at risk surrounding the COVID-19 treatment hospitals in an urban environment- case study: Tehran city

Investigations on the spatial patterns of COVID-19 spreading indicate the possibility of the virus transmission by moving infected people in an urban area. Hospitals are the most susceptible locations due to the COVID-19 contaminations in metropolises. This paper aims to find the riskiest places surrounding the hospitals using an MLP-ANN. The main contribution is discovering the influence zone of COVID-19 treatment hospitals and the main spatial factors around them that increase the prevalence of COVID-19. The innovation of this paper is to find the most relevant spatial factors regarding the distance from central hospitals modeling the risk level of the study area. Therefore, eight hospitals with two service areas for each of them are computed with [0-500] and [500-1000] meters distance. Besides, five spatial factors have been considered, consist of the location of patients' financial transactions, the distance of streets from hospitals, the distance of highways from hospitals, the distance of the non-residential land use from the hospitals, and the hospital patient number. The implementation results revealed a meaningful relation between the distance from the hospitals and patient density. The RMSE and R measures are 0.00734 and 0.94635 for [0-500 m] while these quantities are 0.054088 and 0.902725 for [500-1000 m] respectively. These values indicate the role of distance to central hospitals for COVID-19 treatment. Moreover, a sensitivity analysis demonstrated that the number of patients' transactions and the distance of the non-residential land use from the hospitals are two dominant factors for virus propagation. The results help urban managers to begin preventative strategies to decrease the community incidence rate in high-risk places.

Estimation of the main conditions in (SARS-CoV-2) Covid-19 patients that increase the risk of death using Machine learning, the case of Mexico

Nowadays, society faces a catastrophic problem related to respiratory syndrome due to the coronavirus SARS-CoV-2: the Covid-19 disease. This virus has changed our coexistence rules and, in consequence, has reshaped the daily activities in modern societies. Thus, there are many efforts to understand the virus behaviour in order to reduce its negative impact, and these efforts produce an incredible amount of information and data sources every week. Data scientists, which use techniques such as Machine learning, are focusing their abilities to develop mathematical models for analysing this critical situation. This paper uses Machine Learning techniques as tools to help understand some specific new patterns in Covid patients that arise from unknown complex interactions in the transmission-dynamic models of the SARS-CoV-2 virus, and their relation with the corresponding social contact patterns which are often known or can be inferred from populations variables. One of the main motivations of this research is to find the diseases that cause an increase in the risk of death in infected people with the Covid-19 virus. Mexico is the case of study in this research. The general conditions of health that cause death are well known generally in the world. However, these conditions in each country can differ depending on different factors such as the general health status of people. The results show that the principal causes of death in Mexico are related to age, bad eating habits, chronic diseases, and contact with infected people having not proper care. Results from the analysis show a remarkable accuracy of 87%, which is considered satisfactory.

This is not a pipe - But how harmful is electronic cigarette smoke

This issue of the Biomedical Journal tells us about the risks of electronic cigarette smoking, variations of SARS-CoV-2 and ACE2, and how COVID-19 affects the gastrointestinal system. Moreover, we learn that cancer immunotherapy seems to work well in elderly patients, how thyroid hormones regulate noncoding RNAs in a liver tumour context, and that G6PD is a double-edged sword of redox signalling. We also discover that Perilla leaf extract could inhibit SARS-CoV-2, that artificial neural networks can diagnose COVID-19 patients and predict vaccine epitopes on the Epstein-Barr Virus, and that men and women have differential inflammatory responses to physical effort. Finally, the surgical strategies for drug-resistant epilepsy, computer-supervised double-jaw surgery, dental pulp stem cell motility, and the restitution of the brain blood supply after atherosclerotic stroke are discussed.

Correlation study to identify the factors affecting COVID-19 case fatality rates in India

BACKGROUND AND AIMS

In India, COVID-19 case fatality rates (CFRs) have consistently been very high in states like Punjab and Maharashtra and very low in Kerala and Assam. To investigate the discrepancy in state-wise CFRs, datasets on various factors related to demography, socio-economy, public health, and healthcare capacity have been collected to study their association with CFR.

METHODS

State-wise COVID-19 data was collected till April 22, 2021. The latest data on the various factors have been collected from reliable sources. Pearson correlation, two-tailed P test, Spearman rank correlation, and Artificial Neural Network (ANN) structures have been used to assess the association between various factors and CFR.

RESULTS

Life expectancies, prevalence of overweight, COVID-19 test positive rates, and H1N1 fatality rates show a significant positive association with CFR. Human Development Index, per capita GDP, public affairs index, health expenditure per capita, availability of govt. doctors & hospital beds, prevalence of certain diseases, and comorbidities like diabetes and hypertension show insignificant association with CFR. Sex ratio, health expenditure as a percent of GSDP, and availability of govt. hospitals show a significant negative correlation with CFR.

CONCLUSION

The study indicates that older people, males of younger age groups, and overweight people are at more fatality risk from COVID-19. Certain diseases and common comorbidities like diabetes and hypertension do not seem to have any significant effect on CFR. States with better COVID-19 testing rates, health expenditure, and healthcare capacity seem to perform better with regard to COVID-19 fatality rates.