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AI-Driven Data Analysis for Asthma Risk Prediction.

Meng-Han Chen¹, Guanling Lee¹, Lun-Ping Hung²

¹Department of Computer Science and Information Engineering, National Dong Hua University, Hualien 974301, Taiwan.

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Summary

This summary is machine-generated.

This study developed an AI system using voice analysis to predict asthma. Machine learning models achieved high accuracy, offering a faster, non-invasive diagnostic tool for clinicians.

Keywords:

artificial intelligence (AI)asthma risk prediction machine learning (ML)

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Area of Science:

Otolaryngology
Immunology
Artificial Intelligence
Machine Learning

Background:

Asthma is a global respiratory and immunological condition.
Current asthma diagnosis relies on clinical history, physical exams, and airflow obstruction tests.
Existing diagnostic methods can be invasive and time-consuming, impacting efficiency.

Purpose of the Study:

To develop an AI-based prediction system for asthma diagnosis.
To utilize voice changes associated with asthma-induced respiratory effort.
To create a machine learning (ML)-based clinical decision support tool.

Main Methods:

Analyzed 1500 speech samples (high, normal, low pitch) of phonemes [i, a, u].
Extracted acoustic features using Long-Term Average Spectrum (LTAS) and Single-Frequency Filtering Cepstral Coefficients (SFCCs).
Employed seven ML algorithms to evaluate asthma prediction feasibility.

Main Results:

Decision Tree, CNN, and LSTM models demonstrated accuracies above 0.8 (0.88, 0.80, 0.84 respectively).
Decision Tree excelled in high-pitch phoneme analysis; LSTM performed best for normal and low-pitch phonemes.
Feature importance and spectral analyses validated model efficiency and interpretability.

Conclusions:

AI-driven acoustic analysis can significantly improve asthma diagnosis efficiency.
The developed system offers accurate and reliable decision support for medical clinicians.
This approach provides a non-invasive alternative for asthma detection.