Prediction of Diabetes Using Data Science Technique

Abstract Diabetes is a chronic (long-lasting) health condition that affects how your body turns food into energy. Most of the food you eat is broken down into sugar (also called glucose) and released into your bloodstream. When your blood sugar goes up, it signals your pancreas to release insulin. Diabetes should not be ignored if ignored then diabetes may cause some major issues in a person like heart related problems, kidney problem, blood pressure, eye damage and it can also effect the other organs of human body. So Early prediction of diabetes using various efficient machine learning methods can helps people to prevent the diabetes and to get the treatment earlier. Nowadays machine learning is applied to healthcare system where there is a chance of predicting the disease early. The main necessity of Artificial intelligence is data. In this we have proposed a diabetes prediction model for better classification of diabetes which includes few external factors responsible for diabetes along with regular factors loke glucose, age and BMI, etc. The past dataset is collected and that dataset is used to build a machine learning model. The necessary pre-processing techniques are applied like univariate analysis and bivariate analysis are implemented. The data is visualized for better understanding of the features and based on that a classification model is built by using machine learning algorithm like Decision tree, Support vector, Random Forest, Support vector etc and this comparison of algorithms are done based on their performance metrics like accuracy, F1 score recall etc. we explore various machine learning algorithms which will help in early prediction of this disease