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An estimated 300 million people worldwide suffer from asthma, and this numberis expected to increase to 400 million by 2025. Approximately 250,000 peopledie prematurely each year from asthma out of which, almost all deaths areavoidable. Most of these deaths occur because the patients are unaware of theirasthmatic morbidity. If detected early, asthmatic mortality rate can be reducedby 78%, provided that the patients carry appropriate medication for the sameand/or are in lose vicinity to medical equipment like nebulizers. This studyfocuses on the development and valuation of algorithms to diagnose asthmathrough symptom intensive questionary, clinical data and medical reports.Machine Learning Algorithms like Back-propagation model, Context SensitiveAuto-Associative Memory Neural Network Model, C4.5 Algorithm, Bayesian Networkand Particle Swarm Optimization have been employed for the diagnosis of asthmaand later a comparison is made between their respective prospects. Allalgorithms received an accuracy of over 80%. However, the use of AutoAssociative Memory Model (on a layered Artificial Neural Network) displayedmuch better results. It reached to an accuracy of over 90% and an inconclusivediagnosis 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 madeuse of the self-training auto associative memory model to achieve an accuracyof nearly 94.2%.

A Comprehensive Study on the Applications of Machine Learning for the Medical Diagnosis and Prognosis of Asthma