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Related Experiment Video

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COVID-19 prediction using AI analytics for South Korea.

Adwitiya Sinha¹, Megha Rathi¹

¹Department of Computer Science & Engineering and Information Technology, Jaypee Institute of Information Technology, Noida, Sector-62, Noida, Uttar Pradesh India.

Applied Intelligence (Dordrecht, Netherlands)

|November 12, 2021

Summary

This summary is machine-generated.

This study analyzed COVID-19's demographic impact, finding age significantly increases mortality risk, especially for those 60-80. Machine learning models predict survival chances for infected individuals.

Keywords:

Artificial intelligence Autoencoders COVID-19 Coronavirus pandemic Deep learning Logistic regression Machine learning South Korea Survival prediction

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Area of Science:

Epidemiology
Public Health
Data Science

Background:

The COVID-19 pandemic presented a global public health emergency.
Understanding demographic factors influencing spread and mortality is crucial.

Purpose of the Study:

To analyze demographic factors affecting COVID-19 spread and mortality.
To develop an AI-based model for predicting survival chances in South Korea.
To assess the impact of age, gender, and temporal factors on disease outcomes.

Main Methods:

Demographical analysis of global pandemic spread and mortality.
Cluster-based analysis of age groups.
Application of machine learning and deep learning models with hyperparameter tuning and autoencoder approach.
Statistical analysis of exploratory factors for survival prediction.

Main Results:

Mortality rates increase with age, with the highest death cases in the 60-80 age group.
Association between positive COVID-19 cases and deceased cases identified, with gender-specific impacts.
AI models effectively predicted survival chances for quarantined patients in South Korea.
Machine intelligence and deep learning models provided a quantitative view of the outbreak.

Conclusions:

Age is a critical factor in COVID-19 mortality.
AI and deep learning are valuable tools for analyzing epidemic outbreaks and predicting patient outcomes.
The study offers insights into temporal trends and impactful features of the pandemic.