An Improved COVID-19 Forecasting by Infectious Disease Modelling Using Machine Learning

Ahmad, Hafiz Farooq; Khaloofi, Huda; Azhar, Zahra; Algosaibi, Abdulelah; Hussain, Jamil

doi:10.3390/app112311426

Cited by 7 publications

(2 citation statements)

References 28 publications

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“…Machine learning models can help us understand, predict, and handle COVID-19 datasets in numerous ways. Various research is currently being conducted to forecast COVID-19 cases using various ML models (14) . We examined several well-known forecasting models from the pool of machine learning (ML) models, including Support Vector Machine (SVM) (15) , Random Forest (RF) (12) , Naive Bayes (NB) (16) , Logistic Regression (LR) (17) .…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

Hybrid Feature Selection for COVID-19 Severity Prediction Using Cuckoo Search with SVM Framework

Priya,

Rajeswari

2024

IJST

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Objective:The main objective of this study is to determine the most important blood test markers that may indicate the presence of COVID-19 in a patient. To utilize the Cuckoo Search algorithm with SVM to explore the feature space efficiently and select features that contribute significantly to the model's performance. Methods: A novel hybrid method for feature selection has been proposed with the goal of improving the predictive capabilities of Support Vector Machines (SVM) for determining COVID-19 severity. Blood test datasets are used in the implementation of this study. The dataset has been split into two parts: 80% for training and 20% for testing. First, we use two statistical measures, chi-squared and mutual information, from the filter approach to minimize the feature dimensions. As a wrapper for SVM, we then use a modified Cuckoo Search algorithm. To measure how well the proposed approach works, we used evaluation metrics such as accuracy, precision, recall, and F1 score. Findings: The SVM classifier achieved the best performance with the features obtained from the proposed hybrid method, and the SVM classifier obtained an accuracy of 92% using the blood test dataset. The outcomes demonstrate that our hybrid approach effectively picks a subset of features that makes the model simpler while also making it more accurate and faster to compute. Novelty: This research work proposes a new hybrid feature selection technique by combining filter and wrapper methods to find the best feature set. This combination is introduced for the first time in this type of work related to COVID-19 prediction in which the results of Chi-Square and Mutual Information are used by the modified Cuckoo-Search algorithm to find the top features pertaining to COVID-19 severity and also to improve the performance of SVM model.

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Section: Machine Learning Algorithmsmentioning

confidence: 99%

Hybrid Feature Selection for COVID-19 Severity Prediction Using Cuckoo Search with SVM Framework

Priya,

Rajeswari

2024

IJST

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“…The DL methods promise time-series forecasting, such as the self-regulating comprehension of temporal dependence, and the administration of temporal complexes Bi-LSTM outperforms among DL models, and its forecast values entirely overlap significant numbers of actual cases. Therefore, it performs well with the highest accuracy and is most recommended for disease forecasting (Ahmad et al, 2021). Furthermore, Jayakumar Kaliappan1, Kathiravan Srinivasan1, SaeedMianQaisar, KarpagamSundararajan, Chuan-Yu Chang and Suganthan C (2021), evaluated the performance of Regression Models for the Prediction of the COVID-19 reproduction rate.…”

Section: Review Of Related Literaturementioning

confidence: 99%

Controlling the Spread of Covid-19 in Nigeria Educational Institutions Using Supervised Regression Based Polynomial Model of Degree 4

Nze¹,

Oguike²

2022

Proceedings of the 5th International Conference on the Future of Education

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Coronavirus is a highly infectious disease, caused by the new Corona virus and can spread from person to person through sneezing and coughing droplets. It has signs and symptoms, similar to the common cold but is dangerous and if not reported early and managed by health workers it can cause severe illness in humans and can lead to death. The disease has affected every sector of the economy, including the education sector, as a result, the education sector has devised new methods of delivering teaching and learning in the post COVID-19 era. Therefore, the need to control the spread of the virus cannot be overemphasized. This paper uses Object Oriented Methodology to analyze, design a machine learning system, which was implemented using a python interpreter and a python Integrated Development Environment (IDE) called pycharm. The implementation uses Polynomial Regression model, which is a supervised regression based machine learning model that can be used to control the spread of the virus by predicting the number of incidence of COVID-19 infections and deaths in Nigeria. Sample data dating from 23 rd December 2021 to 8 th May 2022 was used, which gave a high accuracy of 93% in COVID-19 confirmed cases and 100% accuracy in death cases. They both predicted low trend of COVID-19 death cases and confirmed cases in Nigeria in the next one month. This prediction will be very useful to health and government authorities in Nigeria, which have the responsibility of controlling the spread of the virus in Nigeria. It will help them to know how to adopt the various COVID-19 protocols in Nigeria.

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Infectious Disease Modeling: From Traditional to Evolutionary Algorithms

Malhotra,

Goel

2023

Arch Computat Methods Eng

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An Improved COVID-19 Forecasting by Infectious Disease Modelling Using Machine Learning

Cited by 7 publications

References 28 publications

Hybrid Feature Selection for COVID-19 Severity Prediction Using Cuckoo Search with SVM Framework

Hybrid Feature Selection for COVID-19 Severity Prediction Using Cuckoo Search with SVM Framework

Controlling the Spread of Covid-19 in Nigeria Educational Institutions Using Supervised Regression Based Polynomial Model of Degree 4

Infectious Disease Modeling: From Traditional to Evolutionary Algorithms

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