Dynamic Candidate Solution Boosted Beluga Whale Optimization Algorithm for Biomedical Classification

In many fields, complicated issues can now be solved with the help of Artificial Intelligence (AI) and Machine Learning (ML). One of the more modern Metaheuristic (MH) algorithms used to tackle numerous issues in various fields is the Beluga Whale Optimization (BWO) method. However, BWO has a lack of diversity, which could lead to being trapped in local optimaand premature convergence. This study presents two stages for enhancing the fundamental BWO algorithm. The initial stage of BWO's Opposition-Based Learning (OBL), also known as OBWO, helps to expedite the search process and enhance the learning methodology to choose a better generation of candidate solutions for the fundamental BWO. The second step, referred to as OBWOD, combines the Dynamic Candidate Solution (DCS) and OBWO based on the k-Nearest Neighbor (kNN) classifier to boost variety and improve the consistency of the selected solution by giving potential candidates a chance to solve the given problem with a high fitness value. A comparison study with present optimization algorithms for single-objective bound-constraint optimization problems was conducted to evaluate the performance of the OBWOD algorithm on issues from the 2022 IEEE Congress on Evolutionary Computation (CEC'22) benchmark test suite with a range of dimension sizes. The results of the statistical significance test confirmed that the proposed algorithm is competitive with the optimization algorithms. In addition, the OBWOD algorithm surpassed the performance of seven other algorithms with an overall classification accuracy of 85.17% for classifying 10 medical datasets with different dimension sizes according to the performance evaluation matrix.

Predicting the Behavioral Health Needs of Asian Americans in Public Mental Health Treatment: A Classification Tree Approach.

Given the fact that experiencing pandemic-related hardship and racial discrimination worsen Asian Americans' mental health, this study aimed to identify unique characteristics of behavioral health needs among Asian Americans (N = 544) compared to White Americans (N = 78,704) and Black Americans (N = 11,252) who received publicly funded behavioral health services in Indiana before and during the COVID-19 pandemic. We used 2019-2020 Adults Needs and Strengths Assessment (ANSA) data for adults eligible for Medicaid or funding from the state behavioral health agency. Chi-squared automatic interaction detection (CHAID) was used to detect race-specific differences among demographic variables, the pandemic status, and ANSA items. Results indicated that, regardless of age, gender, or pandemic status, Asian Americans who received behavioral health services, struggled more with cultural-related factors compared to White and Black individuals. Within this context, intersections among behavioral/emotional needs (psychosis), life functioning needs (involvement in recovery, residential stability, decision making, medical/physical health), and strengths (job history, interpersonal, and spiritual) further differentiated the mental health functioning of Asian from White and Black Americans. Classification tree algorithms offer a promising approach to detecting complex behavioral health challenges and strengths of populations based on race, ethnicity, or other characteristics.

Deserción universitaria: Evaluación de diferentes algoritmos de Machine Learning para su predicción

University dropout has increased significantly in Peru before and even more so after the COVID-19 pandemic, which is why public universities need to identify and implement programs to reduce it. The purpose of the work is to determine the Machine Learning algorithm that has the best performance to detect university dropout. This analysis was based on the study of university dropouts in Peru between 2018 and 2021. The population is made up of 652 students, 30% were used for training data and 70% for test data from a data set of 106 valid data, for the development of the classification models, the Anaconda Python language was used through its different libraries, the type of research is applied and descriptive design. It was obtained as a result that the K-Nearest-Neighbor algorithm with an accuracy of 0.91, has better performance to predict university dropout with the academic and socioeconomic variables of the students. In conclusion, the model obtained can help predict, in the first cycles of studies, the students most likely to drop out of their studies, as well as alert the welfare office, the need and attention of individual and group tutoring. © 2022. Revista de Ciencias Sociales. All Rights Reserved.

Potential Predictive Value of miR-125b-5p, miR-155-5p and Their Target Genes in the Course of COVID-19.

Purpose: This study aimed to provide new biomarkers for predicting the disease course of COVID-19 by analyzing the dynamic changes of microRNA (miRNA) and its target gene expression in the serum of COVID-19 patients at different stages. Methods: Serum samples were collected from all COVID-19 patients at three time points: the acute stage, the turn-negative stage, and the recovery stage. The expression level of miRNA and the target mRNA was measured by Quantitative Real-Time Polymerase Chain Reaction (RT-qPCR). The classification tree model was established to predict the disease course, and the prediction efficiency of independent variables in the model was analyzed using the receiver operating characteristic (ROC) curve. Results: The expression of miR-125b-5p and miR-155-5p was significantly up-regulated in the acute stage and gradually decreased in the turn-negative and recovery stages. The expression of the target genes CDH5, STAT3, and TRIM32 gradually down-regulated in the acute, turn-negative, and recovery stages. MiR-125b-5p, miR-155-5p, STAT3, and TRIM32 constituted a classification tree model with 100% accuracy of prediction and AUC >0.7 for identification and prediction in all stages. Conclusion: MiR-125b-5p, miR-155-5p, STAT3, and TRIM32 could be useful biomarkers to predict the time nodes of the acute, turn-negative, and recovery stages of COVID-19.

Analysis and Dynamic Monitoring of Indoor Air Quality Based on Laser-Induced Breakdown Spectroscopy and Machine Learning

The air quality of the living area influences human health to a certain extent. Therefore, it is particularly important to detect the quality of indoor air. However, traditional detection methods mainly depend on chemical analysis, which has long been criticized for its high time cost. In this research, a rapid air detection method for the indoor environment using laser-induced breakdown spectroscopy (LIBS) and machine learning was proposed. Four common scenes were simulated, including burning carbon, burning incense, spraying perfume and hot shower which often led to indoor air quality changes. Two steps of spectral measurements and algorithm analysis were used in the experiment. Moreover, the proposed method was found to be effective in distinguishing different kinds of aerosols and presenting sensitivity to the air compositions. In this paper, the signal was isolated by the forest, so the singular values were filtered out. Meanwhile, the spectra of different scenarios were analyzed via the principal component analysis (PCA), and the air environment was classified by K-Nearest Neighbor (KNN) algorithm with an accuracy of 99.2%. Moreover, based on the establishment of a high-precision quantitative detection model, a back propagation (BP) neural network was introduced to improve the robustness and accuracy of indoor environment. The results show that by taking this method, the dynamic prediction of elements concentration can be realized, and its recognition accuracy is 96.5%.

Facilitating Research-Informed Educational Practice for Inclusion. Survey Findings From 147 Teachers and School Leaders in England

This paper considers the engagement by teachers and school leaders in England in educational practices that are both ‘research-informed’ and supportive of inclusive education. We do so by seeking to understand the benefits, costs, and signifying factors these educators associate with research-use. In undertaking the study, we first worked to develop and refine a survey instrument (the ‘Research-Use BCS survey’) that could be used to uniquely and simultaneously measure these concepts. Our survey development involved a comprehensive process that comprised: (1) a review of recent literature;(2) item pre-testing;and (3) cognitive interviews. We then administered this questionnaire to a representative sample of English educators. Although response rates were somewhat impacted by the recent COVID-19 pandemic, we achieved a sufficient number of responses (147 in total) to allow us to engage in descriptive analyses, as well as the production of classification trees. Our analysis resulted in several key findings, including that: (1) if respondents see the benefits of research, they are likely to use it (with the converse also true);(2) if educators have the needed support of their colleagues, they are more likely to use research;and (3) perceiving research-use as an activity that successful teachers and schools engage in is also associated with individual-level research use. We conclude the paper by pointing to potential interventions and strategies that might serve (at least, in the English context) to enhance research-use, so increasing the likelihood of the development and use of effective inclusive practices in schools. Copyright © 2022 Brown, MacGregor, Flood and Malin.

Prevalence and Correlates of Insomnia Symptoms in Older Chinese Adults During the COVID-19 Outbreak: A Classification Tree Analysis.

OBJECTIVE: To examine prevalence and correlates of insomnia symptoms in older Chinese adults (OCAs) during the COVID-19 outbreak. BACKGROUND: During the COVID-19 pandemic, insomnia is a major health concern of elderly individuals, but its subtypes have not been investigated. METHODS: Altogether, 590 OCAs (50+ years) were recruited via snowball sampling during the COVID-19 outbreak. Standardized self-report questions were used to assess the presence of difficulty initiating sleep (DIS), difficulty maintaining sleep (DMS), and early morning awakening (EMA). Classification tree analysis (CTA) was used to identify correlates of insomnia. RESULTS: The one-month prevalence (95% confidence interval) of any subtype of insomnia symptoms was 23.4% (20.0-26.8%), with DIS, DMS, and EMA being 15.4% (12.5-18.3%), 17.1% (14.1-20.2%), and 11.2% (8.64-13.7%), respectively. Worry about being infected with COVID-19 emerged as the most salient correlate of insomnia (P < .001); compared to participants who were not worried about being infected, those who were worried and very worried were 3.2-fold (24.3% vs 7.5%) and 5.5-fold (24.3% vs 7.5%) more likely to have insomnia, respectively. Among participants in the "very worried" branch, those residing in Wuhan were 1.8-fold more likely to have insomnia than those residing in other places (50.0% vs 27.5%, P = .011). Among participants in the "worried" branch, unemployed persons were 2.0-fold more likely to have insomnia than employed persons (37.0% vs 18.1%, P < .001). CONCLUSIONS: Insomnia symptoms were prevalent among OCAs during the COVID-19 outbreak. Selective intervention programs targeting elderly individuals who are worried about being infected, living in the epicenter of COVID-19, and unemployed might be effective.

Pneumonia identification based on lung texture analysis using modified k-nearest neighbour

Covid-19 is a virus that was first discovered in China, which has the impact of mild and severe respiratory infections such as pneumonia. Pneumonia is inflammation and consolidation of lung tissue due to infectious agents. Generally pneumonia has a high mortality rate, as do Covid-19 patients. For now, it is very difficult to distinguish between Pneumonia and Covid-19, due to the high similarity of X-Ray image results. The high similarity has an impact on the difficulty of difference between Pneumonia and Covid-19 patients. This research aims to be able to different Pneumonia and Covid-19 patients based on texture analysis of the Gray Level Co-Occurrence Matrix using Modified k-Nearest Neighbour as a classifier. The calculations used in the Gray Level Co-Occurrence Matrix method are Contrast, Correlation, Energy, and Homogeneity which will be input for the Modified k-Nearest Neighbour classifier. The results showed that the highest accuracy is when the value of K = 3 using Manhattan Distance and 80%:20% data percentage, which is 87.5%. For the values of K = 7 and K = 9 there is no change in accuracy, so it can be concluded that the value of K that affects accuracy only occurs at the values of K = 3 and K = 5. Then, the higher the K value, the lower the resulting accuracy.

Characterization of the level of stress of engineering students using data mining tools

This research addresses the analysis of the level of stress faced by university students of industrial engineering located in metropolitan Lima through data mining tools. In normal situations, the daily load of the student from the eighth to the tenth cycle of a university was divided between university studies and the work of professional practices required in the curriculum, which meant an average load of 25 hours of classes, 30 hours of work in a company and 33 hours of study in the execution of academic tasks per week. This load has been affected since March 15, 2020, when the Ministry of Education established distance education - virtual and the Ministry of Health established confinement due to COVID 19, which impacted on a higher level of stress. The first phase of the research began with data collection, for this phase the SISCO Academic Stress Inventory proposed by Rosanna [1] was used;in the second phase the data preprocessing was carried out;In the third phase, it was identified which are the significant variables that influence a high level of stress measurement of the students, the main methods being the use of logistic regression and the classification tree;In the third phase, the level of precision of the proposed methods were validated, in the logistic regression method a model with a p_value of 95.7%, and a value of the Akaike criterion;In the classification tree method, a precision level of 78% was obtained;Finally, it was determined which are the significant variables that affect the level of stress of the students, such as the ergonomic conditions for studying and carrying out activities at home, which are on average 20 hours a week. The research concludes with the measurement and characterization of the level of stress, recommendations to teachers to be able to motivate students, and look for complementary tools to strengthen learning. © 2021 Latin American and Caribbean Consortium of Engineering Institutions. All rights reserved.

Caracterización del nivel de estrés de alumnos de ingeniería mediante herramientas de Data Mining

This research addresses the analysis of the level of stress faced by university students of industrial engineering located in metropolitan Lima through data mining tools. In normal situations, the daily load of the student from the eighth to the tenth cycle of a university was divided between university studies and the work of professional practices required in the curriculum, which meant an average load of 25 hours of classes, 30 hours of work in a company and 33 hours of study in the execution of academic tasks per week. This load has been affected since March 15, 2020, when the Ministry of Education established distance education - virtual and the Ministry of Health established confinement due to COVID 19, which impacted on a higher level of stress. The first phase of the research began with data collection, for this phase the SISCO Academic Stress Inventory proposed by Rosanna [1] was used;in the second phase the data preprocessing was carried out;In the third phase, it was identified which are the significant variables that influence a high level of stress measurement of the students, the main methods being the use of logistic regression and the classification tree;In the third phase, the level of precision of the proposed methods were validated, in the logistic regression method a model with a p_value of 95.7%, and a value of the Akaike criterion;In the classification tree method, a precision level of 78% was obtained;Finally, it was determined which are the significant variables that affect the level of stress of the students, such as the ergonomic conditions for studying and carrying out activities at home, which are on average 20 hours a week. The research concludes with the measurement and characterization of the level of stress, recommendations to teachers to be able to motivate students, and look for complementary tools to strengthen learning. © 2021 Latin American and Caribbean Consortium of Engineering Institutions. All rights reserved.

Occupational stress among farm and ranch operators in the midwestern United States.

BACKGROUND: This study used surveillance data from 2018 and 2020 to test the stability of work-related strain symptoms (high stress, sleep deprivation, exhaustion) with demographic factors, work characteristics, and musculoskeletal symptoms among farm and ranch operators in seven midwestern states of the United States. METHODS: Cross-sectional surveys were conducted among farm and ranch operators in 2018 (n = 4423) and 2020 (n = 3492). Operators were asked whether, in the past 12 months, they experienced extended work periods that resulted in high stress levels, sleep deprivation, exhaustion/fatigue, or other work-related strain symptoms. Covariates included personal and demographic factors, work characteristics, number of injuries, work-related health conditions, and exposures on the operation. Summary statistics were tabulated for explanatory and outcome variables. The classification (decision) tree approach was used to assess what variables would best separate operators with and without reported strain symptoms, based on a set of explanatory variables. Regularized regression was used to generate effect estimates between the work strain variables and explanatory variables. RESULTS: High stress level, sleep deprivation, and exhaustion were reported more frequently in 2018 than 2020. The classification tree reproduced the 2018 model using 2020 data with approximately 80% accuracy. The mean number of reported MSD symptoms increased slightly from 1.23 in 2018 to 1.41 in 2020. Older age, more time spent in farm work, higher gross farm income (GFI), and MSD symptoms in six body regions (ankles/feet, knees, lower back, neck, shoulders, wrists/hands) were associated with all three work strain symptoms. CONCLUSIONS: Musculoskeletal pain and discomfort was a strong predictor for stress, sleep deprivation, and exhaustion among farmers and ranchers. This finding indicates that reducing MSD pain and discomfort is beneficial for both physical and mental health.

Spread of COVID-19 in Zambia: An assessment of environmental and socioeconomic factors using a classification tree approach.

The global pandemic emergent from SARS-COV-2 (COVID-19) has continued to cause both health and socio-economic challenges worldwide. However, there is limited information on the factors affecting the dynamics of COVID-19, especially in developing countries, including African countries. In this study, we have focused on understanding the association of COVID-19 cases with environmental and socioeconomic factors in Zambia - a sub-Saharan African country. We used Zambia's district-level COVID-19 data, covering 18 March 2020 (i.e., from first reported cases) to 17 July 2020. Geospatial approaches were used to organize, extract and establish the dataset, while a classification tree (CT) technique was employed to analyze the factors associated with the COVID-19 cases. The analyses were conducted in two stages: (1) the binary analysis of occurrences of COVID-19 (i.e., COVID-19 or No COVID-19), and (2) a risk level analysis which grouped the number of cases into four risk levels (high, moderate, low and very low). The results showed that the distribution of COVID-19 cases in Zambia was significantly influenced by the socioeconomic factors compared to environmental factors. More specifically, the binary model showed that distance to the airport, population density and distance to the town centres were the most combination influential factors, while the risk level analysis indicated that areas with high rates of human immuno-deficient virus (HIV) infection had relatively high chances of having many COVID-19 cases compared to areas with low HIV rates. The districts that are far from major urban establishments and that experience higher temperatures have lower chances of having COVID-19 cases. This study makes two major contributions towards the understanding of COVID-19 dynamics: (1) the methodology presented here can be effectively applied in other areas to understand the association of environmental and socioeconomic factors with COVID-19 cases, and (2), the findings from this study present the empirical evidence of the relationship between COVID-19 cases and their associated environmental and socioeconomic factors. Further studies are needed to understand the relationship of this disease and the associated factors in different cultural settings, seasons and age groups, especially as the COVID-19 cases increase and spread in many countries.

Characteristics of patients with Coronavirus Disease 2019 (COVID-19) and seasonal influenza at time of hospital admission: a single center comparative study.

BACKGROUND: In the future, co-circulation of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and influenza viruses A/B is likely. From a clinical point of view, differentiation of the two disease entities is crucial for patient management. We therefore aim to detect clinical differences between Coronavirus Disease 2019 (COVID-19) and seasonal influenza patients at time of hospital admission. METHODS: In this single-center observational study, we included all consecutive patients hospitalized for COVID-19 or influenza between November 2019 and May 2020. Data were extracted from a nationwide surveillance program and from electronic health records. COVID-19 and influenza patients were compared in terms of baseline characteristics, clinical presentation and outcome. We used recursive partitioning to generate a classification tree to discriminate COVID-19 from influenza patients. RESULTS: We included 96 COVID-19 and 96 influenza patients. Median age was 68 vs. 70 years (p = 0.90), 72% vs. 56% (p = 0.024) were males, and median Charlson Comorbidity Index (CCI) was 1 vs. 2 (p = 0.027) in COVID-19 and influenza patients, respectively. Time from symptom onset to hospital admission was longer for COVID-19 (median 7 days, IQR 3-10) than for influenza patients (median 3 days, IQR 2-5, p < 0.001). Other variables favoring a diagnosis of COVID-19 in the classification tree were higher systolic blood pressure, lack of productive sputum, and lack of headache. The tree classified 86/192 patients (45%) into two subsets with ≥80% of patients having influenza or COVID-19, respectively. In-hospital mortality was higher for COVID-19 patients (16% vs. 5%, p = 0.018). CONCLUSION: Discriminating COVID-19 from influenza patients based on clinical presentation is challenging. Time from symptom onset to hospital admission is considerably longer in COVID-19 than in influenza patients and showed the strongest discriminatory power in our classification tree. Although they had fewer comorbidities, in-hospital mortality was higher for COVID-19 patients.

A classification tree to assist with routine scoring of the Clinical Frailty Scale.

BACKGROUND: the Clinical Frailty Scale (CFS) was originally developed to summarise a Comprehensive Geriatric Assessment and yield a care plan. Especially since COVID-19, the CFS is being used widely by health care professionals without training in frailty care as a resource allocation tool and for care rationing. CFS scoring by inexperienced raters might not always reflect expert judgement. For these raters, we developed a new classification tree to assist with routine CFS scoring. Here, we test that tree against clinical scoring. OBJECTIVE/METHODS: we examined agreement between the CFS classification tree and CFS scoring by novice raters (clerks/residents), and the CFS classification tree and CFS scoring by experienced raters (geriatricians) in 115 older adults (mean age 78.0 ± 7.3; 47% females) from a single centre. RESULTS: the intraclass correlation coefficient (ICC) for the CFS classification tree was 0.833 (95% CI: 0.768-0.882) when compared with the geriatricians' CFS scoring. In 93%, the classification tree rating was the same or differed by at most one level with the expert geriatrician ratings. The ICC was 0.805 (0.685-0.883) when CFS scores from the classification tree were compared with the clerk/resident scores; 88.5% of the ratings were the same or ±1 level. CONCLUSIONS: a classification tree for scoring the CFS can help with reliable scoring by relatively inexperienced raters. Though an incomplete remedy, a classification tree is a useful support to decision-making and could be used to aid routine scoring of the CFS.

Risk factors and action thresholds for the novel coronavirus pandemic. Insights from the Italian Society of Nephrology COVID-19 Survey.

BACKGROUND AND AIM: Over 80% (365/454) of the nation's centers participated in the Italian Society of Nephrology COVID-19 Survey. Out of 60,441 surveyed patients, 1368 were infected as of April 23rd, 2020. However, center-specific proportions showed substantial heterogeneity. We therefore undertook new analyses to identify explanatory factors, contextual effects, and decision rules for infection containment. METHODS: We investigated fixed factors and contextual effects by multilevel modeling. Classification and Regression Tree (CART) analysis was used to develop decision rules. RESULTS: Increased positivity among hemodialysis patients was predicted by center location [incidence rate ratio (IRR) 1.34, 95% confidence interval (CI) 1.20-1.51], positive healthcare workers (IRR 1.09, 95% CI 1.02-1.17), test-all policy (IRR 5.94, 95% CI 3.36-10.45), and infected proportion in the general population (IRR 1.002, 95% CI 1.001-1.003) (all p < 0.01). Conversely, lockdown duration exerted a protective effect (IRR 0.95, 95% CI 0.94-0.98) (p < 0.01). The province-contextual effects accounted for 10% of the total variability. Predictive factors for peritoneal dialysis and transplant cases were center location and infected proportion in the general population. Using recursive partitioning, we identified decision thresholds at general population incidence ≥ 229 per 100,000 and at ≥ 3 positive healthcare workers. CONCLUSIONS: Beyond fixed risk factors, shared with the general population, the increased and heterogeneous proportion of positive patients is related to the center's testing policy, the number of positive patients and healthcare workers, and to contextual effects at the province level. Nephrology centers may adopt simple decision rules to strengthen containment measures timely.

Perceived vulnerability to COVID-19 infection from event attendance: results from Louisiana, USA, two weeks preceding the national emergency declaration.

BACKGROUND: Individual perceptions of personal and national threats posed by COVID-19 shaped initial response to the pandemic. The aim of this study was to investigate the changes in residents' awareness about COVID-19 and to characterize those who were more aware and responsive during the early stages of the pandemic in Louisiana. METHODS: In response to the mounting threat of COVID-19, we added questions to an ongoing food preference study held at Louisiana State University from March 3rd through March 12th, 2020. We asked how likely it was that the spread of the coronavirus will cause a national public health crisis and participants' level of concern about contracting COVID-19 by attending campus events. We used regression and classification tree analysis to identify correlations between these responses and (a) national and local COVID case counts; (b) personal characteristics and (c) randomly assigned information treatments provided as part of the food preference study. RESULTS: We found participants expressed a higher likelihood of an impending national crisis as the number of national and local confirmed cases increased. However, concerns about contracting COVID-19 by attending campus events rose more slowly in response to the increasing national and local confirmed case count. By the end of this study on March 12th, 2020 although 89% of participants agreed that COVID-19 would likely cause a public health crisis, only 65% of the participants expressed concerns about contracting COVID-19 from event attendance. These participants were significantly more likely to be younger students, in the highest income group, and to have participated in the study by responding to same-day, in-person flyer distribution. CONCLUSIONS: These results provide initial insights about the perceptions of the COVID-19 public health crisis during its early stages in Louisiana. We concluded with suggestions for universities and similar institutions as in-person activities resume in the absence of widespread vaccination.