0% Complete
Home
/
11th International Conference on Computer and Knowledge Engineering
Predicting the Recovery Rate of COVID-19 Using a Novel Hybrid Method
Authors :
Fatemeh Ahouz
1
Ebrahim Sayahi
2
1- Behbahan Khatam Alanbia University of Technology Behbahan, Iran
2- Shiraz University, Iran
Keywords :
COVID-19, Prediction Model, Data Mining, Biased Data, Recovery Rate
Abstract :
Abstract— COVID-19 pandemic and its transformation into a global health emergency have further highlighted the need to design an intelligent system that can analyze the growing information of such an epidemic. At the beginning of the outbreak, due to the lack of information about the factors affecting the recovery of people compared to the information of infected or dead ones, designing a system based on such biased data that can accurately predict the recovered cases is very important. Such a system can help health officials make decisions in complex situations and reduce public anxiety. In this study, a new hybrid structure for predicting the recovery rate of COVID-19 is presented. This structure, which is also suitable for binary classification tasks in medical applications, is a combination of algorithms with high sensitivity and specificity criteria. The COVID-19 dataset provided by Johns Hopkins University was used to evaluate the performance of the model. We used the data from the first 43 days of outbreak in 160 different regions around the world. The accuracy, sensitivity and specificity of the model on test set were 84.54%, 78.53% and 88.73%, respectively. These promising results show that the model can be used to analyze other medical data on which the learning algorithms produce biased results.
Papers List
List of archived papers
Automating Theory of Mind Assessment with a LLaMA-3-Powered Chatbot: Enhancing Faux Pas Detection in Autism
Avisa Fallah - Ali Keramati - Mohammad Ali Nazari - Fatemeh Sadat Mirfazeli
Intelligent Interpretation of Frequency Response Signatures to Diagnose Radial Deformation in Transformer Windings Using Artificial Neural Network
Reza Behkam - Hossein Karami - Mehdi Salay Naderi - Gevork B. Gharehpetian
Deep Learning Based High-Resolution Edge Detection for Microwave Imaging using a Variational Autoencoder
Seyed Reza Razavi Pour - Leila Ahmadi - Amir Ahmad Shishegar
IR-LPR: Large Scale of Iranian License Plate Recognition Dataset
Mahdi Rahmani - Melika Sabaghian - Seyyedeh Mahila Moghadami - Mohammad Mohsen Talaie - Mahdi Naghibi - Mohammad Ali Keyvanrad
Data Clustering using Chimp Optimization Algorithm
SAYED PEDRAM HAERI BOROUJENI - ELNAZ PASHAEI
Optimizing Question-Answering Framework Through Integration of Text Summarization Model and Third-Generation Generative Pre-Trained Transformer
Ervin Gubin Moung - Toh Sin Tong - Maisarah Mohd Sufian - Valentino Liaw - Ali Farzamnia - Farashazillah Yahya
Divide and Conquer Approach to Long Genomic Sequence Alignment
Mahmoud Naghibzadeh - Samira Babaei - Behshid Behkmal - Mojtaba Hatami
A Genetic-based Fusion Approach of Persian and Universal Phonetic results for Spoken Language Identification
Ashkan Moradi - Yasser Shekofteh - Saeed Zarei
An Adaptive Budget and Deadline-aware Algorithm for Scheduling Workflows Ensemble in IaaS Clouds
Negin Shafinezhad - Hamid Abrishami - Saeid Abrishami
Real-Time Gender Recognition with a Deep Neural Network
Samad Azimi Abriz - Majid Meghdadi
more
Samin Hamayesh - Version 41.3.1