Feature Scaling Beats Architecture Choice in Offline Respiratory Screening for Low-Resource Clinics

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feature scalingclass imbalancelow-resource settingssound classification

2610.33030

Builder Relevance

80%

1h ago

Abstract

The paper compares classical and deep classifiers for respiratory sound classification in low-resource clinics, highlighting the importance of feature scaling over model architecture.

Reality Card

Core Claim

Standardizing features for an SVM significantly improved macro recall from 0.51 to 0.78, demonstrating that preprocessing has a larger impact than model architecture.

Method / Result

An SVM with standardized features achieved a macro recall of 0.78.

Limitations

The main limitation is the confounding effect of recording sources on label assignment, which may affect reproducibility.

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