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

Muhammad Nazrul Islam¹, Md Shofiqul Islam², Nahid Hasan Shourav²

¹Department of Computer Science and Engineering, Military Institute of Science and Technology, Mirpur Cantonment, Dhaka, 1216, Bangladesh. nazrul@cse.mist.ac.bd.

Scientific Reports

|April 30, 2024

Summary

Post-COVID-19 recovery is impacted by demographics and health factors like sleep and memory. Machine learning, particularly Decision Trees, effectively identifies key predictors of long-term health issues after COVID-19 infection.

Keywords:

COVID-19 Chi-square Machine learning Pandemic Pearson’s coefficient Statistical analysis

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Area of Science:

Medical Research
Public Health
Data Science

Background:

COVID-19, an infectious respiratory disease, presents diverse health outcomes, including long-term sequelae.
Many recovered individuals experience persistent health issues influenced by demographic, physiological, and neurological factors.

Purpose of the Study:

To investigate health factors affecting diverse demographic profiles in post-COVID-19 patients.
To establish correlations among physiological and neurological factors in the post-COVID-19 state.
To identify the most effective machine learning model for predicting post-COVID-19 health impacts.

Main Methods:

Survey data collected from COVID-recovered patients in Bangladesh.
Application of various machine learning algorithms to predict post-COVID-19 factors.
Statistical validation using Chi-square and Pearson's coefficient.
Feature importance analysis using Gini Index, Feature Coefficients, Information Gain, and SHAP Value Assessment.

Main Results:

Significant relationships identified among post-COVID-19 health factors.
Pearson's coefficient indicated associations among physiological and neurological factors.
The Decision Tree model demonstrated superior performance in identifying crucial predictive features for post-COVID-19 impact.

Conclusions:

Demographic and health factors significantly influence post-COVID-19 recovery and long-term health.
Machine learning models, especially Decision Trees, are effective tools for understanding and predicting post-COVID-19 sequelae.
Identifying key features aids in targeted interventions for improved patient outcomes.