Abstract

The classification of respiratory diseases is an important problem that most researchers

have tried to solve directly by using deep learning. Traditional machine learning with

handcrafted features has been left behind and is less explored in this context, although it

may offer efficiency and interpretability. In this paper, we performed a comparison be-

tween multiple machine learning algorithms. We applied the algorithms on respiratory

sound data from the ICBHI Dataset. We extracted various features from the data, features

that are more suitable for signals. We used the features both individually and together.

The task was a classification one, both binary and multiclass. We ran each algorithm sep-

arately with the feature sets. For each algorithm, we performed more than 1200 runs using

different parameters to optimize their learning and overall performance. Random Forest

performed best, showing very promising results with accuracy and a F1 score of almost

80%, closely followed by k Nearest Neighbors. Other algorithms stood out only with cer-

tain features, while others returned suboptimal results. This experiment showed that a good

choice of features, preprocessing and hyperparameter optimization play a very important

role in classic machine learning that remains competitive in certain scenarios.

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