Prediction and diagnosis of diabetes using machine learning classifiers. (21st June 2022)

• Record Type: Journal Article
• Authors: Tyagi, Harsh; Agarwal, Aditya; Gupta, Aakash; Goel, Kanak; Srivastava, Anand Kumar; Srivastava, Akhilesh Kumar
• Is Part Of: International journal of forensic software engineering. Volume 1:Number 4(2022)
• Page Start: 335
• Page End: 347
• Publication Date: 2022-06-21
• Subjects: machine learning -- diabetes -- random forest -- XGBoot -- decision tree
• Languages: English
• ISSNs: 1743-5099

Abstract

Major portion of diabetes in the world is of type 2 due to environmental conditions and lifestyle. If the diabetes is predicted at an early stage, it would really help in reducing its effects by the use of early medication. This article is based on machine learning model to predict diabetes based on diagnostic measurements. Machine learning can play an essential role in predicting presence/absence of diabetes mellitus (type 2 diabetes). The article presents the ML-based approach for prediction of the diabetes that makes use of algorithms like XGBoost, decision tree, random forest. In this, medical data of user is used as input and prediction of diabetes is done using mentioned algorithms. The output (prediction) will be the ensemble of the output of all three algorithms. That way, all the algorithms are used to make predictions and to establish a comparison between the accuracy obtained from these methods.