A Systematic Review: Forecasting Post-Pandemic Health Trends with Machine Learning Methods
Keywords:
COVID-19, Pandemic, Machine Learning, PredictionAbstract
Purpose Over the last few months, the spread of Coronavirus has become a global concern, affecting every corner of the world. While scientists are working tirelessly to discover a cure, the exact cause of this outbreak remains unclear. With the surge in the number of cases requiring testing for Coronavirus, conventional methods are becoming increasingly challenging due to constraints in time and resources. In recent times, machine learning has proven to be highly effective in the field of medicine. Implementing machine learning techniques to predict COVID-19 in patients could significantly accelerate the process of obtaining test results. This would enable healthcare workers to promptly administer appropriate medical care, thus improving the management of the pandemic.
Objectives: The primary objective of this thesis is to create a machine learning model capable of predicting COVID-19 infection in patients. This involves conducting a comprehensive literature review to determine the most appropriate algorithm. Additionally, the study aims to evaluate the various factors that influence the effectiveness of the prediction model.
Methods: A thorough Systematic Literature Review was conducted to pinpoint the optimal algorithms for the predictive model. Following this, a review was constructed based on the insights gained from the literature review, specifically for predicting COVID-19. This review also aims to ascertain the key features that influence its predictive accuracy.
Proposed Results: The literature review identified several algorithms suitable for prediction, including SVM, Random Forests, and ANNs. To determine the most accurate technique, a performance comparison was conducted among these algorithms. Additionally, feature importance values were calculated to assess their influence on the predictive outcomes.
Conclusions: Applying Machine Learning to forecast the aftermath of COVID-19 has the potential to expedite illness detection, leading to a decrease in fatality rate. Upon analyzing the findings acquired from studies, it was determined that algorithms exhibited superior responsiveness.
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