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
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