  An estimated 300 million people worldwide suffer from asthma, and this number
is expected to increase to 400 million by 2025. Approximately 250,000 people
die prematurely each year from asthma out of which, almost all deaths are
avoidable. Most of these deaths occur because the patients are unaware of their
asthmatic morbidity. If detected early, asthmatic mortality rate can be reduced
by 78%, provided that the patients carry appropriate medication for the same
and/or are in lose vicinity to medical equipment like nebulizers. This study
focuses on the development and valuation of algorithms to diagnose asthma
through symptom intensive questionary, clinical data and medical reports.
Machine Learning Algorithms like Back-propagation model, Context Sensitive
Auto-Associative Memory Neural Network Model, C4.5 Algorithm, Bayesian Network
and Particle Swarm Optimization have been employed for the diagnosis of asthma
and later a comparison is made between their respective prospects. All
algorithms received an accuracy of over 80%. However, the use of Auto
Associative Memory Model (on a layered Artificial Neural Network) displayed
much better results. It reached to an accuracy of over 90% and an inconclusive
diagnosis rate of less than 1% when trained with adequate data. In the end,
na\"ive mobile based applications were developed on Android and iOS that made
use of the self-training auto associative memory model to achieve an accuracy
of nearly 94.2%.
