ArticleResults in physics2022
Comparative study of artificial neural network versus parametric method in COVID-19 data analysis.
Article in Results in physics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- Artificial intelligence and machine learning in the development of vaccines and immunotherapeutics-yesterday, today, and tomorrow.Frontiers in artificial intelligence · 2025Review
- Identification of mitochondria-related feature genes for predicting type 2 diabetes mellitus using machine learning methods.Frontiers in endocrinology · 2025Article
- Article
- A historical review and analysis on MOORA and its fuzzy extensions for different applications.Heliyon · 2024Article
- Recurrent neural network for the dynamics of Zika virus spreading.AIMS public health · 2024Article
- Artificial neural network-assisted prediction of radiobiological indices in head and neck cancer.Frontiers in artificial intelligence · 2024Article
- IoT platforms assessment methodology for COVID-19 vaccine logistics and transportation: a multi-methods decision making model.Scientific reports · 2023Article
- Combining the dynamic model and deep neural networks to identify the intensity of interventions during COVID-19 pandemic.PLoS computational biology · 2023Article
- An innovative ensemble model based on deep learning for predicting COVID-19 infection.Scientific reports · 2023Article
- Prediction of the morbidity and mortality rates of COVID-19 in Egypt using non-extensive statistics.Scientific reports · 2023Article
- A Hybrid Model Based on Improved Transformer and Graph Convolutional Network for COVID-19 Forecasting.International journal of environmental research and public health · 2022Article
- Estimating the Dissolution of Anticancer Drugs in Supercritical Carbon Dioxide with a Stacked Machine Learning Model.Pharmaceutics · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Since the previous two years, a new coronavirus (COVID-19) has found a major global problem. The speedy pathogen over the globe was followed by a shockingly large number of afflicted people and a gradual increase in the number of deaths. If the survival analysis of active individuals can be predicted, it will help to contain the epidemic significantly in any area. In medical diagnosis, prognosis and survival analysis, neural networks have been found to be as successful as general nonlinear models. In this study, a real application has been developed for estimating the COVID-19 mortality rates in Italy by using two different methods, artificial neural network modeling and maximum likelihood estimation. The predictions obtained from the multilayer artificial neural network model developed with 9 neurons in the hidden layer were compared with the numerical results. The maximum deviation calculated for the artificial neural network model was -0.14% and the R value was 0.99836. The study findings confirmed that the two different statistical models that were developed had high reliability.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.