Predicting the Medals of the Countries Participating in the Tokyo 2020 Olympic Games Using the Test of Networks of Multilayer Perceptron (MLP)

Fazlollahi, Pouya; Afarineshkhaki, Akbar; Nikbakhsh, Reza

doi:10.29252/aassjournal.799

Cited by 4 publications

(2 citation statements)

References 23 publications

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“…The components of the models are nodes, weights, and layers (input, hidden, and output layers) (41). MLP-ANN is the simplest and most commonly used ANN architecture due to its structural flexibility, good representational capabilities, and a large number of training algorithms [47,48]. In this study, in order to develop an MLP-ANN, we used 12 training algorithms, including Levenberg-Marquardt (LM), Bayesian regularization (BR), Broyden-Fletcher-Goldfarb-Shanno (BFGS) Quasi-Newton, resilient backpropagation (RP), scaled conjugate gradient (SGC), conjugate gradient with Powell/ Beale (CGB) restarts, conjugate gradient Fletcher-Powell (CGF), conjugate gradient with Polak-Ribiére (CGP) updates, one step secant (OSS), gradient descent variable learning rate (GDX), gradient descent with momentum (GDM), and gradient descent (GD) backpropagation described in Table 2.…”

Section: Model Developmentmentioning

confidence: 99%

Comparing artificial neural network training algorithms to predict length of stay in hospitalized patients with COVID-19

et al. 2022

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Background The exponential spread of coronavirus disease 2019 (COVID-19) causes unexpected economic burdens to worldwide health systems with severe shortages in hospital resources (beds, staff, equipment). Managing patients’ length of stay (LOS) to optimize clinical care and utilization of hospital resources is very challenging. Projecting the future demand requires reliable prediction of patients’ LOS, which can be beneficial for taking appropriate actions. Therefore, the purpose of this research is to develop and validate models using a multilayer perceptron-artificial neural network (MLP-ANN) algorithm based on the best training algorithm for predicting COVID-19 patients' hospital LOS. Methods Using a single-center registry, the records of 1225 laboratory-confirmed COVID-19 hospitalized cases from February 9, 2020 to December 20, 2020 were analyzed. In this study, first, the correlation coefficient technique was developed to determine the most significant variables as the input of the ANN models. Only variables with a correlation coefficient at a P-value < 0.2 were used in model construction. Then, the prediction models were developed based on 12 training algorithms according to full and selected feature datasets (90% of the training, with 10% used for model validation). Afterward, the root mean square error (RMSE) was used to assess the models’ performance in order to select the best ANN training algorithm. Finally, a total of 343 patients were used for the external validation of the models. Results After implementing feature selection, a total of 20 variables were determined as the contributing factors to COVID-19 patients’ LOS in order to build the models. The conducted experiments indicated that the best performance belongs to a neural network with 20 and 10 neurons in the hidden layer of the Bayesian regularization (BR) training algorithm for whole and selected features with an RMSE of 1.6213 and 2.2332, respectively. Conclusions MLP-ANN-based models can reliably predict LOS in hospitalized patients with COVID-19 using readily available data at the time of admission. In this regard, the models developed in our study can help health systems to optimally allocate limited hospital resources and make informed evidence-based decisions.

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Section: Model Developmentmentioning

confidence: 99%

Comparing artificial neural network training algorithms to predict length of stay in hospitalized patients with COVID-19

et al. 2022

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“…The components of the models are nodes, weights, and layers (input, hidden, and output layers) (41). Multilayer perceptron-ANN (MLP-ANN) is the simplest and most commonly used ANN architecture due to its structural exibility, good representational capabilities, and a large number of trainable algorithms (30,31).…”

Section: Model Developmentmentioning

confidence: 99%

Predictive modeling of hospital Length of Stay in COVID-19 patients using Artificial Neural Networks

Orooji

Shanbehzadeh

Kazemi-Arpanahi³

et al. 2021

Preprint

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BackgroundThe current pandemic of coronavirus disease (COVID-19) causes unexpected economic burdens to worldwide health organizations with severe shortages in hospital bed capacity and other related medical resources. Therefore, predicting the length of stay (LOS) is essential to ensure optimal allocating scarce hospital resources and inform evidence-based decision-making. Thus, the purpose of this research is to construct a model for predicting COVID-19 patients' hospital LOS by multiple multilayer perceptron-artificial neural network (MLP-ANN) algorithms. Material and MethodsUsing a single-center registry, the records of 1225 laboratory-confirmed COVID-19 hospitalized cases from February 9, 2020, to December 20, 2020, were analyzed. The correlation coefficient technique was developed to determine the most significant variables as the input of the ANN models. Only variables with a correlation coefficient at the P-value< 0.2 were used in model construction. Ultimately the prediction models were developed based on 12 ANN techniques according to selected variables. ResultsAfter implementing feature selection, a total of 20 variables was determined as the most relevant predictors to build the models. The results indicated that the best performance belongs to a neural network with 20 and 10 neurons in the hidden layer of the Bayesian Regularization classifier for whole and selected features with RMSE of 1.6213 and 2.2332, respectively. ConclusionThe developed model in this study can help in the better calculation of LOS in COVID-19 patients. This model also can be leveraged in hospital bed management and optimized resource utilization.

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Analysis of countries' performances in individual Olympic Games using cluster analysis and decision trees: the case of Tokyo 2020

Cetinkaya,

Peker,

Kuvvetli

2024

SBM

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PurposeThe purpose of this study is to investigate and understand the performance of countries in individual Olympic Games, specifically focusing on the Tokyo 2020 Olympics. Employing cluster analysis and decision trees, the research aims to categorize countries based on their representation, participation and success.Design/methodology/approachThis research employs a data-driven approach to comprehensively analyze and enhance understanding of countries' performances in individual Olympic Games. The methodology involves a two-stage clustering method and decision tree analysis to categorize countries and identify influential factors shaping their Olympic profiles.FindingsThe study, analyzing countries' performances in the Tokyo 2020 Olympics through cluster analysis and decision trees, identified five clusters with consistent profiles. Notably, China, Great Britain, Japan, Russian Olympic Committee and the United States formed a high-performing group, showcasing superior success, representation and participation. The analysis revealed a correlation between higher representation/participation and success in individual Olympic Games. Decision tree insights underscored the significance of population size, GDP per Capita and HALE index, indicating that countries with larger populations, better economic standing and higher health indices tended to perform better.Research limitations/implicationsThe study has several limitations that should be considered. Firstly, the findings are based on data exclusively from the Tokyo 2020 Olympics, which may limit the generalizability of the results to other editions.Practical implicationsThe research offers practical implications for policymakers, governments and sports organizations seeking to enhance their country's performance in individual Olympic Games.Social implicationsThe research holds significant social implications by contributing insights that extend beyond the realm of sports.Originality/valueThe originality and value of this research lie in its holistic approach to analyzing countries' performances in individual Olympic Games, particularly using a two-stage clustering method and decision tree analysis.

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Predicting the Medals of the Countries Participating in the Tokyo 2020 Olympic Games Using the Test of Networks of Multilayer Perceptron (MLP)

Cited by 4 publications

References 23 publications

Comparing artificial neural network training algorithms to predict length of stay in hospitalized patients with COVID-19

Comparing artificial neural network training algorithms to predict length of stay in hospitalized patients with COVID-19

Predictive modeling of hospital Length of Stay in COVID-19 patients using Artificial Neural Networks

Analysis of countries' performances in individual Olympic Games using cluster analysis and decision trees: the case of Tokyo 2020

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