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An expert fitness diagnosis system based on elastic cloud computing.

Kevin C Tseng1, Chia-Chuan Wu2

  • 1Product Design and Development Laboratory, Department of Industrial Design, College of Management, Chang Gung University, 259 Wenhua 1st Road, Guishan Shiang, Taoyuan 33302, Taiwan ; Healthy Aging Research Center, Chang Gung University, Taiwan.

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Summary

This study introduces a cloud-based expert system using machine learning for personalized fitness diagnoses. Naïve Bayes achieved 90.8% accuracy, and an elastic algorithm optimized cloud resource allocation for improved service quality.

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Area of Science:

  • Cloud Computing
  • Machine Learning
  • Health Informatics

Background:

  • Expert diagnosis systems are crucial for personalized health assessments.
  • Cloud computing offers scalable infrastructure for data-intensive applications.
  • Supervised machine learning techniques can effectively classify user data.

Purpose of the Study:

  • To develop an expert diagnosis system for classifying user fitness levels.
  • To implement a cloud-based platform for customized health assessments.
  • To present an elastic algorithm for dynamic computation resource allocation.

Main Methods:

  • Utilized supervised machine learning techniques for fitness level classification.
  • Integrated physiological data (age, gender, BMI) for personalized diagnoses.
  • Developed an elastic algorithm based on Poisson distribution for resource allocation, using exponential moving average for prediction.

Main Results:

  • Naïve Bayes demonstrated the highest classification accuracy at 90.8%.
  • The elastic algorithm effectively predicted and allocated computation resources based on request trends.
  • The system ensured quality of service through dynamic resource management.

Conclusions:

  • The proposed cloud-based expert system accurately diagnoses fitness levels using machine learning.
  • The elastic resource allocation algorithm enhances system performance and reliability.
  • This approach offers a scalable and effective solution for personalized health informatics.