Early Stage Diabetes Prediction by Approach Using Machine Learning Techniques

Abstract

Abstract Diabetes is the most viral and chronic disease throughout the world. A large number of people are affected by this chronic disease. Early detection of diabetes in a patient is crucial for ensuring a good quality of life. Machine learning techniques or Data Mining Techniques are playing a significant role in today’s life to detect diabetes and improve performance to make further accurate predictions. The aim of this research is diabetes prediction with the approach of machine learning techniques. In this technical approach, we have taken two data sets Pi-ma Indian diabetes data set and the Kaggle diabetes data set, and proposed a model for diabetes prediction. We have used four different machine learning algorithms such as Support Vector Machine, Decision Forest, Linear Regression, and Artificial Neural Network. In these machine learning algorithms, ANN gives the best prediction performance where the highest accuracy is 98.8% so, it could be used as an alternative method to support predict diabetes complication diseases at an initial stage. Further, this work can be extended to find how likely non-diabetic people can have diabetes in the next few years and also, this predicted model can be used for imaging processing in the future to find diabetes for the prediction of diabetic and non-diabetic.