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Exploring post-COVID-19 health effects and features with advanced machine learning techniques. | LitMetric

AI Article Synopsis

  • COVID-19 has resulted in diverse health outcomes for patients, with many recovering but still facing negative health effects influenced by factors like age and gender, as well as emotional and cognitive issues.
  • The research investigates these health factors by collecting survey data from recovered COVID-19 patients in Bangladesh and employs machine learning algorithms to identify significant correlations and predict health outcomes.
  • The Decision Tree model emerged as the most effective in determining key features and predicting the impact of post-COVID-19 health issues through various analytical methods.

Article Abstract

COVID-19 is an infectious respiratory disease that has had a significant impact, resulting in a range of outcomes including recovery, continued health issues, and the loss of life. Among those who have recovered, many experience negative health effects, particularly influenced by demographic factors such as gender and age, as well as physiological and neurological factors like sleep patterns, emotional states, anxiety, and memory. This research aims to explore various health factors affecting different demographic profiles and establish significant correlations among physiological and neurological factors in the post-COVID-19 state. To achieve these objectives, we have identified the post-COVID-19 health factors and based on these factors survey data were collected from COVID-recovered patients in Bangladesh. Employing diverse machine learning algorithms, we utilised the best prediction model for post-COVID-19 factors. Initial findings from statistical analysis were further validated using Chi-square to demonstrate significant relationships among these elements. Additionally, Pearson's coefficient was utilized to indicate positive or negative associations among various physiological and neurological factors in the post-COVID-19 state. Finally, we determined the most effective machine learning model and identified key features using analytical methods such as the Gini Index, Feature Coefficients, Information Gain, and SHAP Value Assessment. And found that the Decision Tree model excelled in identifying crucial features while predicting the extent of post-COVID-19 impact.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11589696PMC
http://dx.doi.org/10.1038/s41598-024-60504-wDOI Listing

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