Intelligent systems for diagnosis of different types of diabetes

Diabetes is a disease which often occurs when blood sugar of a person is high or more then the threshold value. There are many types of diabetes such as pre diabetes, type 1 and type 2 diabetes. Diabetes also have other adverse effects. It should be treated well on time. In developing areas, the doctor to patient ratio is very small. So medical diagnostic systems are helpful in these situations. Medical diagnostic systems also give the results similar to the expert or doctor. Symptoms are given as inputs to these medical diagnostic systems and they give the results. They can act as supporting tool for the patients and physicians as well. Further there are many techniques which can be applied to these medical diagnostic systems. These techniques are normally artificial intelligence techniques. These techniques contain mainly machine learning and deep learning techniques. A large number of authors have already worked on these kind of medical diagnostic systems for diabetes. Authors have used soft computing, machine learning and deep learning techniques for diagnosis of various different diseases such as diabetes. In this review paper, the contribution made by different authors in field of medical diagnostic system for diabetes is stated. This paper describes different techniques applied by different authors for the diagnosis of diabetes.