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Asthma Detection Research Based on Voice Signal Processing and Machine Learning

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Published on: July 22, 2025

Application of Apriori Improvement Algorithm in Asthma Case Data Mining.

Yi Zheng¹, Peipei Chen¹, Biyu Chen¹

¹Department of Respiratory and Critical Care Medicine, Taihe Hospital, Hubei University of Medicine, Shiyan 442000, Hubei Province, China.

Journal of Healthcare Engineering

|November 11, 2021

Summary

This summary is machine-generated.

This study introduces an enhanced Apriori algorithm for analyzing Chinese medicine asthma data. The improved algorithm efficiently mines associations between symptoms and medications, outperforming traditional methods.

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Area of Science:

Traditional Chinese Medicine
Data Mining
Computational Health

Background:

Asthma case data in Chinese medicine is rich with empirical information from clinical diagnoses.
Data correlation analysis is crucial for identifying associations between prescriptions, prescribers, symptoms, and medications.

Purpose of the Study:

To analyze the performance of the Apriori algorithm for mining medical case data.
To propose an extended Apriori algorithm optimized for medical data analysis.
To compare the efficacy of the proposed algorithm against existing methods.

Main Methods:

Analysis of the traditional Apriori algorithm's limitations in medical data mining.
Development of an extended Apriori algorithm leveraging bit-string logic operations.
Comparative evaluation focusing on running time, frequent itemset mining, and association rule discovery.

Main Results:

The extended Apriori algorithm demonstrates superior performance compared to existing algorithms.
Effective association analysis of asthma medication and combined symptom-medication data.
Validation of the algorithm's effectiveness through experimental and simulation results.

Conclusions:

The proposed extended Apriori algorithm offers significant improvements for analyzing complex medical datasets, particularly in Traditional Chinese Medicine.
The identified association relationships between asthma symptoms and medications hold substantial clinical application value.
This approach provides a robust computational tool for evidence-based medicine in asthma management.