Imagine you're a doctor. You need to know if a patient's cough or sneeze is just a passing thing or a sign of something bigger. That's where machine learning comes in. Researchers created an automatic system to spot sneezes and coughs, sounds that can hint at respiratory problems. The goal? Early diagnosis and quick treatment.

This project took on a challenge called the "Pfizer digital medicine challenge" on a platform called "OSFHOME". The team used two big sound datasets, ESC-50 and AudioSet, which together have over 3800 different sounds like phones ringing and kids laughing. They looked for features that could separate coughs and sneezes from the rest.

To do this, they used a method called Mel frequency cepstral coefficients (MFCC). It turns sounds into numbers computers can understand. These numbers were then fed into different mathematical models to spot the patterns specific to coughs and sneezes.

The researchers tried three different techniques to classify these sounds. One used a support vector machine with a special kind of math called radial basis function (RBF) kernels. This technique worked best, spotting coughs and sneezes 83% of the time.

This study shows how AI can help doctors catch problems early. But it also raises questions. Like, what if the sound is different depending on the person's age or health condition? Or what if someone has more than one type of respiratory issue? These are questions for future studies.

Sounds in Health: Spotting Respiratory Issues with AI

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