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A Prediction Model of Health Development Based on Linear Sequential Extreme Learning Machine Algorithm Matrix.

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  • 1College of Mathematics and Statistica, Chongqing Technology and Business University, Chongqing 400067, China.

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Summary
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This study introduces a novel health prediction model using a hybrid approach. The improved algorithm enhances the accuracy of public health development predictions for diverse individuals.

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Area of Science:

  • Public Health
  • Health Informatics
  • Computational Intelligence

Background:

  • Increasing living pressure from socio-economic development negatively impacts public health.
  • Accurate health development prediction models are crucial for addressing these concerns.
  • Existing models may lack the precision needed for diverse individual health evaluations.

Purpose of the Study:

  • To develop an accurate model for predicting public health development.
  • To enhance the precision of health status evaluation for large datasets and individual needs.
  • To integrate advanced computational techniques for improved health analysis.

Main Methods:

  • Utilized the linear sequential extreme learning machine (ELM) algorithm for health status evaluation.
  • Introduced rough set theory to the linear sequential ELM algorithm.
  • Developed a hybrid model combining ELM and rough set theory for enhanced analysis.

Main Results:

  • The improved linear sequential ELM algorithm demonstrated accurate analysis of mass health data.
  • The model effectively predicted health development for different individuals.
  • Enhanced evaluation accuracy was achieved for large-scale health assessments.

Conclusions:

  • The integration of rough set theory significantly improves the predictive accuracy of the ELM algorithm for health development.
  • The developed model meets the needs of mass health evaluation and individual health status prediction.
  • This approach offers a robust solution for public health monitoring and personalized health management.