MODELLING A VOTING-BASED MODEL FOR DIABETES PREDICTION USING LEARNING MODELS

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

NCSST-2021

AICTE Sponsored National Conference on Smart Systems and Technologies

Last Date: 25th November 2021

LATEST INNOVATION’S AND FUTURE TRENDS IN MANAGEMENT

Last Date: 7th November 2021

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