Abstract:
"
Diabetes mellitus is a chronic disease which has victimized millions of people around the world.
Due to the significance and increasing number of diabetes patients over the past years, a classifier
to detect diabetes with an optimal cost and an improved performance has become a need. Under
this circumstance mHealth (mobile health) applications play a major role. Similarly, as to diagnose
diabetes, nowadays many decision support systems are available which consume different
techniques and approaches to detect diabetes. Although they claim to offer numerous personalized
resolutions and cost-effective health promotion; it has been declared that they do pose an unseen
and a dangerous risk towards the user by consuming user sensitive data.
Therefore, the author introduced the proposed solution ‘Diabetor’ - Cross-Platform Diabetes
Predictor using Federated Learning Approach. For this, extensive research was conducted
regarding privacy risks related to mHealth applications. As well as the utilization of privacy
preservation deep learning techniques such as Federated Learning to optimize the privacy of user
sensitive health data when predicting diabetes. A comprehensive literature search, technological
analysis and a data flow process has been presented regarding this process. The process has been
streamlined with the use of Feedforward ANN built with PyTorch library and SMC to encrypt the
data on training and inference stages.
Finally, after the development and testing phases of the prototype, the author successfully had the
solution evaluated by expert and non-expert evaluators that provided their feedback on the
application and regarded it as a timely and useful solution.
"
