Predicting Respiratory Diseases from audio base input of coughing and sneezes.

The health economic models related to Asthma

MPH Capstone Data Management & Analysis

Smart, portable spirometer with real-time lung function analysis and mobile app integration for accessible respiratory health monitoring.

AI-powered respiratory disease detection using deep learning on audio spectrograms | Published IEEE Research | Multi-class classification of 8 respiratory conditions including COPD, Asthma, and Pneumonia

Lung Sound Classification Models

Interactive healthcare analytics dashboard built in Excel analyzing respiratory disease trends, treatment patterns, recovery outcomes, and state-wise variation using Pivot Tables, slicers, and KPI cards.

A Python-based dataset of high-quality respiratory sound recordings, annotated for machine learning tasks focused on detecting lung conditions like wheezes and crackles. It includes preprocessed audio, annotations, and subject metadata for research in respiratory health.

A full-stack deep learning application that analyzes lung or breath sounds to detect diseases like asthma, COPD, and pneumonia using spectrograms and a trained CNN model. Built using Flask, TensorFlow, and Librosa

Analysis of lung health measures from populations in Nepal, Peru and Uganda as part of the Global Excellence in COPD Outcomes-1 (GECo1) study. Funding: MRC (MR/P008984/1) & NIHR (303125). Paper: Alayadhi et al. 10.1183/13993003.01830-2025

Code to accompany "Use of Patient-Reported Symptom Data in Clinical Decision Rules for Predicting Influenza in a Telemedicine Setting" by Billings et al, JABFM 2023

Android app for tracking symptoms in chronic respiratory disease patients. Built at BitsxLaMarató 2024.