ArticleDiagnostics (Basel, Switzerland)2023
On the Implementation of the Artificial Neural Network Approach for Forecasting Different Healthcare Events.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 41 citations in OpenAlex.
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- An intelligent ensemble machine learning model for early detection of chronic kidney disease in aging populations.Scientific reports · 2026Article
- Clinical Application of Machine Learning Models for Early-Stage Chronic Kidney Disease Detection.Diagnostics (Basel, Switzerland) · 2025Article
- Leveraging dynamics informed neural networks for predictive modeling of COVID-19 spread: a hybrid SEIRV-DNNs approach.Scientific reports · 2025Article
- Forecasting cardiovascular disease mortality using artificial neural networks in Sindh, Pakistan.BMC public health · 2025Article
- Global burden and forecast of infectious diseases attributable to drug use: evidence from GBD 2021.Frontiers in public health · 2025Article
- Cloud-based real-time enhancement for disease prediction using Confluent Cloud, Apache Kafka, feature optimization, and explainable artificial intelligence.PeerJ. Computer science · 2025Article
- Optimizing public health management with predictive analytics: leveraging the power of random forest.Frontiers in big data · 2025Article
- A hybrid AI approach for predicting academic performance in RBE students.Frontiers in artificial intelligence · 2025Article
- Forecasting global monthly cotton prices: the superiority of NNAR models over traditional models.Frontiers in artificial intelligence · 2025Article
- Evaluating the effectiveness of self-attention mechanism in tuberculosis time series forecasting.BMC infectious diseases · 2024Article
- A comprehensive analysis of the artificial neural networks model for predicting monkeypox outbreaks.Heliyon · 2024Article
- Optimal features selection in the high dimensional data based on robust technique: Application to different health database.Heliyon · 2024Review
- Modeling and forecasting carbon dioxide emission in Pakistan using a hybrid combination of regression and time series models.Heliyon · 2024Article
- Evaluating the forecasting performance of ensemble sub-epidemic frameworks and other time series models for the 2022-2023 mpox epidemic.Royal Society open science · 2024Article
- Short-Term Forecasting of Monkeypox Cases Using a Novel Filtering and Combining Technique.Diagnostics (Basel, Switzerland) · 2023Article
- MRpoxNet: An enhanced deep learning approach for early detection of monkeypox using modified ResNet50.Digital healthArticle
- Article
Corrections and comments
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Authors and funding
6 authors at 5 institutions in 3 countries.
Funding
Abstract
The rising number of confirmed cases and deaths in Pakistan caused by the coronavirus have caused problems in all areas of the country, not just healthcare. For accurate policy making, it is very important to have accurate and efficient predictions of confirmed cases and death counts. In this article, we use a coronavirus dataset that includes the number of deaths, confirmed cases, and recovered cases to test an artificial neural network model and compare it to different univariate time series models. In contrast to the artificial neural network model, we consider five univariate time series models to predict confirmed cases, deaths count, and recovered cases. The considered models are applied to Pakistan's daily records of confirmed cases, deaths, and recovered cases from 10 March 2020 to 3 July 2020. Two statistical measures are considered to assess the performances of the models. In addition, a statistical test, namely, the Diebold and Mariano test, is implemented to check the accuracy of the mean errors. The results (mean error and statistical test) show that the artificial neural network model is better suited to predict death and recovered coronavirus cases. In addition, the moving average model outperforms all other confirmed case models, while the autoregressive moving average is the second-best model.
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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.