  As witnessed in the past year, where the world was brought to the ground by a
pandemic, fighting Life-threatening diseases have found greater focus than
ever. The first step in fighting a disease is to diagnose it at the right time.
Diabetes has been affecting people for a long time and is growing among people
faster than ever. The number of people who have Diabetes reached 422 million in
2018, as reported by WHO, and the global prevalence of diabetes among adults
above the age of 18 has risen to 8.5%. Now Diabetes is a disease that shows no
or very few symptoms among the people affected by it for a long time, and even
in some cases, people realize they have it when they have lost any chance of
controlling it. So getting Diabetes diagnosed at an early stage can make a huge
difference in how one can approach curing it. Moving in this direction in this
paper, we have designed a liquid machine learning approach to detect Diabetes
with no cost using deep learning. In this work, we have used a dataset of 520
instances. Our approach shows a significant improvement in the previous
state-of-the-art results. Its power to generalize well on small dataset deals
with the critical problem of lesser data in medical sciences.
