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arXiv AI By Abubakar Chilala, Cleophas Kadima, Innocent Anyaele, Victor Olufemi

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

Read the original on arXiv AI →

The study compares classical and deep learning classifiers for a five‑class respiratory‑sound dataset of 1,211 clips. An SVM using 30 summary features (MFCC, ZCR, RMS) achieves macro recall of 0.78, while a 1.13M‑parameter convolutional‑recurrent network reaches 0.75–0.77; no clear advantage is found between model families. The largest performance gain comes from standardizing the SVM’s features, which boosts macro recall by 27 points, far exceeding any architectural difference.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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