Pneumonia Prediction using AI

Author(s)	Mr. Amit Kumar Singh
Country	India
Abstract	Pneumonia is a leading global cause of morbidity and mortality, particularly in children and the elderly. Traditional diagnostics often rely on imaging and laboratory tests, which can delay treatment in resource-limited settings. This study proposes an AI-based system for pneumonia prediction using respiratory sounds recorded through digital stethoscopes. Acoustic features, including MFCCs and spectral patterns, were extracted and analyzed using deep learning models (CNNs and RNNs). The system achieved over 93% accuracy, demonstrating performance comparable to experienced clinicians. This portable, cost-effective approach has strong potential for early detection and integration into telemedicine platforms
Keywords	Pneumonia detection Artificial Intelligence (AI) Digital stethoscope Respiratory sound analysis Deep learning Convolutional Neural Network (CNN) Recurrent Neural Network (RNN) Auscultation MFCC (Mel-Frequency Cepstral Coefficients) Point-of-care diagnostics Telemedicine Low-resource healthcare Early disease prediction
Field	Medical / Pharmacy
Published In	Volume 7, Issue 5, September-October 2025
Published On	2025-09-15
DOI	https://doi.org/10.36948/ijfmr.2025.v07i05.55850

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