Early Diabetes Prediction Using Machine Learning


  • Ambuja K
  • Anusha G S
  • Sangeetha T D
  • Sharath Kumar N
  • Sumanth K Naik


Machine learning, Random Forest, Accuracy.


Diabetes is a chronic disease with the potential to cause a worldwide health care crisis. Diabetes is an illness caused because of high glucose level in a human body. Diabetes should not be ignored if it is untreated then Diabetes may cause some major issues in a person like: heart related problems, kidney problem, blood pressure, eye damage and it can also affect other organs of human body. Diabetes can be controlled if it is predicted earlier. Diabetes is one of the fastest-growing diseases in the world and requires constant monitoring. To verify this, we are exploring different machine learning algorithms that will help with this baseline prediction. achieve this goal this project work we will do early prediction of Diabetes in a human body or a patient for a higher accuracy through applying, Various Machine Learning Techniques. Machine learning techniques Provide better result for prediction by constructing models from datasets collected from patients. In this work we will use Machine Learning Classification and ensemble techniques on a dataset to predict diabetes. We are using Random Forest (RF) for model building. The Project work gives the accurate or higher accuracy model shows that the model is capable of predicting diabetes effectively. We are expecting our result to show that Random Forest achieved higher accuracy. The accuracy of this model is above 90%.


Download data is not yet available.




How to Cite

Ambuja K, Anusha G S, Sangeetha T D, Sharath Kumar N, & Sumanth K Naik. (2023). Early Diabetes Prediction Using Machine Learning. International Journal of Progressive Research in Science and Engineering, 4(5), 24–28. Retrieved from https://journal.ijprse.com/index.php/ijprse/article/view/830