Development of COVID-19 Health-Risk Assessment and Self-Evaluation (CHaSe): a health screening system for university students and staff during the movement control order (MCO)

Ismail, Azimah; Ibrahim, Mohd Salami; Ismail, Samhani; Aziz, Azwa Abdul; Yusoff, Harmy Mohamed; Mokhtar, Mokhairi; Juahir, Hafizan

doi:10.1007/s13721-022-00357-3

Cited by 3 publications

(4 citation statements)

References 17 publications

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“…On the other hand, [13] investigated the use of principal component analysis (PCA) in clinical aspect. To measure the effectiveness of reducing infection risk among university student, a pilot study of 200 volunteers was carried out.…”

Section: A Filter Methodsmentioning

confidence: 99%

Hybrid Feature Selection Algorithm and Ensemble Stacking for Heart Disease Prediction

Zaini¹,

Awang²

2023

IJACSA

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In cardiology, as in other medical specialties, early and accurate diagnosis of heart disease is crucial as it has been the leading cause of death over the past few decades. Early prediction of heart disease is now more crucial than ever. However, the state-of-the-art heart disease prediction strategy put more emphasis on classifier selection in enhancing the accuracy and performance of heart disease prediction, and seldom considers feature reduction techniques. Furthermore, there are several factors that lead to heart disease, and it is critical to identify the most significant characteristics in order to achieve the best prediction accuracy and increase prediction performance. Feature reduction reduces the dimensionality of the information, which may allow learning algorithms to work quicker and more efficiently, producing predictive models with the best rate of accuracy. In this study, we explored and suggested a hybrid of two distinct feature reduction techniques, chi-squared and analysis of variance (ANOVA). In addition, using the ensemble stacking method, classification is performed on selected features to classify the data. Using the optimal features based on hybrid features combination, the performance of a stacking ensemble based on logistic regression yields the best result with 93.44%. This can be summarized as the feature selection method can take into account as an effective method for the prediction of heart disease.

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Section: A Filter Methodsmentioning

confidence: 99%

Hybrid Feature Selection Algorithm and Ensemble Stacking for Heart Disease Prediction

Zaini¹,

Awang²

2023

IJACSA

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“…Previously, the selection of predictors in the original CHaSe model began with an extensive search of the literature and real COVID-19 case database during the initial phase of the pandemic [ 5 ]. Subsequently, the selection and validation of the important predictors were conducted via principal component analyses, discriminant analyses, and medical experts’ validation from a pilot and a real nationwide screening of the target population [ 4 ]. As a result, a total of 11 important predictors (designated with letter-code a to k ) and outcomes of three risk categories were shown in Table 1 and Table 2 , respectively.…”

Section: Methodsmentioning

confidence: 99%

“…CHaSe is a screening system accessible via mobile apps and web browsers. CHaSe is also extended as a surveillance system for active case detection when all UniSZA staff and students have been asked to complete daily risk assessments using CHaSe prediction model for every working day [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

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Medical Experts’ Agreement on Risk Assessment Based on All Possible Combinations of the COVID-19 Predictors—A Novel Approach for Public Health Screening and Surveillance

Ibrahim

Naing

Aziz

et al. 2022

IJERPH

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During the initial phase of the coronavirus disease 2019 (COVID-19) pandemic, there was a critical need to create a valid and reliable screening and surveillance for university staff and students. Consequently, 11 medical experts participated in this cross-sectional study to judge three risk categories of either low, medium, or high, for all 1536 possible combinations of 11 key COVID-19 predictors. The independent experts’ judgement on each combination was recorded via a novel dashboard-based rating method which presented combinations of these predictors in a dynamic display within Microsoft Excel. The validated instrument also incorporated an innovative algorithm-derived deduction for efficient rating tasks. The results of the study revealed an ordinal-weighted agreement coefficient of 0.81 (0.79 to 0.82, p-value < 0.001) that reached a substantial class of inferential benchmarking. Meanwhile, on average, the novel algorithm eliminated 76.0% of rating tasks by deducing risk categories based on experts’ ratings for prior combinations. As a result, this study reported a valid, complete, practical, and efficient method for COVID-19 health screening via a reliable combinatorial-based experts’ judgement. The new method to risk assessment may also prove applicable for wider fields of practice whenever a high-stakes decision-making relies on experts’ agreement on combinations of important criteria.

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Mathematical Modelling to Predict the Effect of Vaccination on Delay and Rise of COVID-19 Cases Management

et al. 2023

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In this paper, a mathematical model based on COVID-19 is developed to study and manage disease outbreaks. The effect of vaccination with regard to its efficacy and percentage of population vaccinated in a closed population is investigated. To study virus transmission, the system employs six nonlinear ordinary differential equations with susceptible–exposed–asymptomatic–infected–vaccinated–recovered populations and the basic reproduction number are calculated. The proposed model describes for highly infectious diseases (such as COVID-19) in a closed containment area with no migration. This paper considers that the percentage of vaccinated population has a significant impact on the number of COVID-19 positive cases during the pandemic wave and examines how the pandemic rise time is delayed. Numerical simulation to investigate disease outbreaks when the community is undergoing vaccination is performed, taking the efficacy rate of the vaccine into account. Sensitivity Index values are calculated for the reproduction number and their relations with few other parameters are depicted.

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Development of COVID-19 Health-Risk Assessment and Self-Evaluation (CHaSe): a health screening system for university students and staff during the movement control order (MCO)

Cited by 3 publications

References 17 publications

Hybrid Feature Selection Algorithm and Ensemble Stacking for Heart Disease Prediction

Hybrid Feature Selection Algorithm and Ensemble Stacking for Heart Disease Prediction

Medical Experts’ Agreement on Risk Assessment Based on All Possible Combinations of the COVID-19 Predictors—A Novel Approach for Public Health Screening and Surveillance

Mathematical Modelling to Predict the Effect of Vaccination on Delay and Rise of COVID-19 Cases Management

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