Enhanced pediatric pneumonia auxiliary diagnosis: Integrating optical fiber vibration sensing with machine learning

Childhood pneumonia remains a primary cause of death in children under five. Early detection is arduous due to its inconspicuous symptoms, and the existing radiological diagnostic techniques carry the risk of radiation - induced harm to young patients. To overcome these limitations, a groundbreaking study has proposed an innovative non - invasive diagnostic approach that integrates fiber optic vibration sensing technology with machine learning algorithms for pediatric pneumonia diagnosis. A novel fiber optic sensor is engineered to precisely capture respiratory vibration signals (RVS). These signals are then processed and analyzed using a Stacked - Grid Search Ensemble Learning Model (SGELM). In the experiment, respiratory vibration signals were gathered from 1649 pediatric patients aged between 3 and 14 who suffered from respiratory diseases. Through data balancing techniques, the dataset was expanded to 2184 samples. This dataset was partitioned into training, testing, and validation subsets. The developed system exhibited remarkable performance on the test dataset. It achieved high levels of accuracy, sensitivity, and specificity. Notably, it was also capable of classifying different pneumonia pathological types and statuses. This innovative method not only mitigates the radiation - related risks associated with traditional diagnostic methods but also holds great promise in revolutionizing the diagnosis of pediatric respiratory diseases. It could potentially improve the early - diagnosis rate and contribute to better treatment outcomes for children with pneumonia, thus playing a significant role in enhancing child health globally.