Abstract:
Diabetes has become a common disease in mankind of the young and the old
person. According to the national survey 2017-18 on diabetes, overall, around 37.5
million people above 20 are suffering from this disease in the country.[1] Every 4th
Pakistani is suffering from diabetes.[2] So, it is badly needed to develop a system that
can effectively predict the diabetes using medical details. We can help reduce risk of
Diabetes by giving suggestions to make changes in your lifestyle. Common risk factors
include increased weight, blood pressure, cholesterol and triglyceride (blood fat) levels.
Changing the habits of a lifetime isn’t easy, but it’s worth the effort. We propose a
strategy for the prediction of diabetes using deep learning by training its attributes. We
propose a strategy for the prediction of diabetes using deep learning by training its
attributes.
The idea of deep learning is fast-growing and it works quite like a human mind.
It represents the data in multiple levels and able to solve the selectivity-invariance
dilemma efficiently [3]. Deep learning techniques are used in a variety of forms in
field of medical prognosis. Many research works prove that deep learning techniques
provide a better outcome, declines the classification error rate and more robust to noise
than other strategies. It can handle massive amount of data and have capability to
decode a complex problem in an easy way. We are using Recurrent Neural Network
(RNN). This system explores approaches to improve the accuracy using medical data
with Deep learning algorithms
