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@article{171797,
        author = {Kiran Nagayya Hiremath and Manoj N R and Shashank P B and Suresh B and Rajesh T H},
        title = {Respiratory Disease Prediction Using Respiratory Sound},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {8},
        pages = {3261-3265},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=171797},
        abstract = {This project presents a novel approach to predicting respiratory diseases using voice analysis and machine learning. By leveraging the nuances in vocal characteristics, such as pitch, harmonics, and spectral features, the system aims to identify conditions like Chronic Obstructive Pulmonary Disease (COPD] asthma, and pneumonia from voice recordings. The methodology involves collecting voice data preprocessing it to enhance quality, and extracting relevant features using techniques like Mel-spectrograms. These features are then used to train a Random Forest classifier, a robust machine learning model known for its accuracy and reliability. The trained model is capable of analyzing new voice inputs to predict respiratory diseases.},
        keywords = {Sound Analysis, Respiratory disease prediction, Random Forest Algorithm, Feature Extraction, Non-Invasive Diagnostics.},
        month = {January},
        }