Diagnosis of hyperglycemia using Artificial Neural Networks IJTSRD
The aim of Artificial Intelligence is to develop the machines to perform the tasks in a better way than the humans. Another aim of Artificial Intelligence is to understand the actions whether it occurs in humans, machines or animals. As a result, Artificial Intelligence is gaining Importance in science and engineering fields. The use of Artificial Intelligence in medical diagnosis too is becoming increasingly common and has been used widely in the diagnosis of cancers, tumors, hepatitis, lung diseases, etc… The main aim of this paper is to build an Artificial Intelligent System that after analysis of certain parameters can predict that whether a person is diabetic or not. Diabetes is the name used to describe a metabolic condition of having higher than normal blood sugar levels. Diabetes is becoming increasingly more common throughout the world, due to increased obesity – which can lead to metabolic syndrome or pre-diabetes leading to higher incidences of type 2 diabetes. Authors have identified 10 parameters that play an important role in diabetes and prepared a rich database of training data which served as the backbone of the prediction algorithm. Keeping in view this training data authors developed a system that uses the artificial neural networks algorithm to serve the purpose. These are capable of predicting new observations (on specific variables) from previous observations (on the same or other variables) after executing a process of so-called learning from existing training data (Haykin1998).The results indicate that the ANN is the best predictor with the accuracy of about 96%. This system can be used to assist medical programs especially in geographically remote areas where expert human diagnosis not possible with an advantage of minimal expenses and faster results.
By Abid Sarwar”Diagnosis of hyperglycemia using Artificial Neural Networks”
Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017,
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