Predictive model for Diabetes The Silent Killer
Adarsh Jaiswal, Dishant Kumbhar, Samruddhi Patil
KNN ,Logistics Regression, Machine Learning, SVM
Diabetes is a disease caused due to the increase level of blood glucose. Diabetes is a chronic disease with the potential to cause a worldwide health care crisis. According to International Diabetes Federation 382 million people are living with diabetes across the whole world. By 2035, this will be doubled as 592 million. Various traditional methods, based on physical and chemical tests, are available for diagnosing diabetes. However, early prediction of diabetes is quite challenging task for medical practitioners due to complex interdependence on various factors as diabetes affects human organs such as kidney, eye, heart, nerves, foot etc. Data science methods have the potential to benefit other scientific fields by shedding new light on common questions. One such task is to help make predictions on medical data. Machine learning is an emerging scientific field in data science dealing with the ways in which machines learn from experience. The aim of this project is to develop a system which can perform early prediction of diabetes for a patient with a higher accuracy by combining the results of machine learning technique. This project aims to predict diabetes via supervised machine learning methods Logistic regression. This project also aims to propose an effective technique for earlier detection of the diabetes disease.
Article Details
Unique Paper ID: 155726

Publication Volume & Issue: Volume 9, Issue 1

Page(s): 1553 - 1556
Article Preview & Download

Share This Article

Conference Alert


AICTE Sponsored National Conference on Smart Systems and Technologies

Last Date: 25th November 2021

SWEC- Management


Last Date: 7th November 2021

Latest Publication

Go To Issue

Call For Paper

Volume 8 Issue 4

Last Date 25 September 2021

About Us enables door in research by providing high quality research articles in open access market.

Send us any query related to your research on

Social Media

Google Verified Reviews

Contact Details