Diabetes Disease Prediction Using Artificial Intelligence

For a long time, the major problem area for researchers is disease diagnosis and the main interest of the medicine is an accurate diagnosis. Many engineering techniques have been developed in the past to help the medical staff with a diagnosis tool. There are many traditional methods of disease diagnosis, but the application of machine learning techniques has given a new dimension to this area. In this work, two different approaches have been used for the purpose of classification between diabetic and non-diabetic, using Pima Indian Diabetes Dataset. Principal Component Analysis has been used in the purpose of feature dimension reduction before applying any proposed classifier. Support Vector Machine (SVM) and Naïve Bayes (NB) are the two classifiers used in our study. 94.14 % and 93.88% are the accuracies obtained for the SVM and NB approaches.The results obtained are very interesting and show improvement from the previous works. With this accurate learning technique, there is enough scope for improvement considerably in this field.