Developing a Machine Learning Algorithm to Determine COVID-19 Contamination in Different Age Groups and Comparing Statistical Algorithms and Learning Data

doi:10.1109/ACCESS.2024.3447835

2024-01-01

Galip Savas Ilgi

İlker Etikan

Yoney Kirsal Ever *

DOI：10.1109/ACCESS.2024.3447835

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The development of machine learning algorithms for the correction checking of statistical method theorems is a significant advancement in statistical research and analysis. These algorithms are designed to automatically detect and rectify errors and inconsistencies in applying statistical theorems, improving the overall reliability and accuracy of statistical analyses. These algorithms can identify issues in interpreting and applying statistical theorems by leveraging machine learning techniques, such as natural language processing, pattern recognition, and data validation. As a result, they help researchers and analysts avoid potential pitfalls, enhance the quality of statistical results, and streamline the peer-review process in scientific publications. This innovative approach combines the power of automation with the intricacies of statistical theory, promising more robust and error-free statistical analyses in various research domains.

Keyword:

Machine learning algorithms

COVID-19

Statistical analysis

Data models

Analytical models

Machine learning

Prediction algorithms

Big Data

Big data

data mining

machine learning algorithms