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Asthma Detection Research Based on Voice Signal Processing and Machine Learning
Published on: July 22, 2025
Fuzzy rule-based expert system for evaluating level of asthma control
Maryam Zolnoori1, Mohammad Hosain Fazel Zarandi, Mostafa Moin
1Department of Information Technology Management, Tarbiat Modares University, Tehran, Iran. m.zolnoori@gmail.com
This study introduces an intelligent fuzzy system to improve asthma control assessment. The system accurately evaluates asthma control using 14 variables, aiding physicians in better patient management and reducing asthma-related complications.
Area of Science:
- Medical Informatics
- Pulmonology
- Artificial Intelligence
Background:
- Asthma control is crucial for therapy, yet physician oversight and variable uncertainty lead to underestimation.
- Underestimation of asthma control contributes to increased morbidity and mortality.
Purpose of the Study:
- To develop an intelligent fuzzy system for accurate asthma control assessment.
- To address the limitations of current methods in evaluating asthma control levels.
Main Methods:
- An intelligent fuzzy system was designed using 14 input variables across five modules: respiratory symptoms severity, bronchial obstruction, asthma instability, current treatment, and quality of life.
- The system outputs a degree of asthma control on a score of 0-10.
Main Results:
- The fuzzy system's performance was evaluated with 42 asthma patients.
- Results aligned with expert physician evaluations, indicating high accuracy.
- The system identified subtle differences in asthma control levels among patients.
Conclusions:
- The intelligent fuzzy system offers a reliable solution for assessing asthma control.
- This approach can enhance clinical decision-making and potentially improve patient outcomes in asthma management.
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