MODELLING A VOTING-BASED MODEL FOR DIABETES PREDICTION USING LEARNING MODELS
Author(s):
DR.R. MURUGANANTHAM, M..SOWMYASREE, A.SAI RUSHIK, G.BHANU KIRAN
Keywords:
Diabetes, learning approaches, feature analysis, prediction, accuracy
Abstract
Diabetes mellitus is defined as a collection of metabolic problems that significantly impact human health worldwide. Wide-ranging study into all aspects of diabetes (diagnostic, pathophysiology, therapy, etc.) has ushered in an era of massive amounts of data. This investigation aims to provide a prediction model using machine learning, data analysis methodologies and tools in diabetic prediction. The primary goal of this work is to design a method that can more accurately predict diabetes in patients. Here, a novel ensemble model is evaluated using several characteristics such as precision, accuracy, F-measure, and recall. The machine-learning techniques are identified after hyper-tuning and cross- validation (CV) and then employed in the Vote-based ensemble model ( ). According to the findings, the proposed framework can get an excellent result of approximately 92% accuracy.
Article Details
Unique Paper ID: 155471

Publication Volume & Issue: Volume 9, Issue 1

Page(s): 986 - 991
Article Preview & Download


Share This Article

Conference Alert

NCSST-2021

AICTE Sponsored National Conference on Smart Systems and Technologies

Last Date: 25th November 2021

SWEC- Management

LATEST INNOVATION’S AND FUTURE TRENDS IN MANAGEMENT

Last Date: 7th November 2021

Go To Issue



Call For Paper

Volume 8 Issue 4

Last Date 25 September 2021

About Us

IJIRT.org enables door in research by providing high quality research articles in open access market.

Send us any query related to your research on editor@ijirt.org

Social Media

Google Verified Reviews

Contact Details

Telephone:6351679790
Email: editor@ijirt.org
Website: ijirt.org

Policies