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Updated: Nov 2, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Predicting the incidence of COVID-19 using data mining.

Fatemeh Ahouz¹, Amin Golabpour²

¹Department of Computer Engineering, School of Engineering, Behbahan Khatam Alanbia University of Technology, Behbahan, Iran.

BMC Public Health

|June 8, 2021

Summary

This summary is machine-generated.

This study developed a COVID-19 incidence prediction model using a Least-Square Boosting Classification algorithm. The model accurately forecasts global confirmed cases within two weeks, aiding disease management.

Keywords:

COVID-19 Data mining Predicting Prevalence

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Area of Science:

Epidemiology
Data Science
Public Health

Background:

The global prevalence of COVID-19 necessitates accurate forecasting for effective public health decision-making.
Predicting disease incidence is crucial for managing the ongoing pandemic and allocating resources.

Purpose of the Study:

To develop and evaluate a predictive model for COVID-19 incidence.
To forecast COVID-19 cases within a two-week timeframe to aid disease management strategies.

Main Methods:

Utilized daily updated COVID-19 case data from Johns Hopkins University for 252 regions.
Employed a Least-Square Boosting Classification algorithm, analyzing regional and neighboring data from the preceding two weeks.
Developed a model to predict incidence rates for the subsequent two weeks.

Main Results:

The model achieved a high overall accuracy of 98.45% in predicting global confirmed COVID-19 cases.
Mean absolute errors for incidence rate groups were 4.71% (<200), 8.54% (200-1000), and 6.13% (>1000).

Conclusions:

A boosting-based model effectively predicts COVID-19 incidence by analyzing regional and neighboring data patterns.
Accurate short-term forecasting of COVID-19 incidence supports proactive public health interventions and disease control.