Paper Contents
Abstract
Abstract Diabetes affects kidney disease, eyesight loss and heart disease in addition to being the worlds largest cause of mortality. By assisting with precise disease diagnosis and treatment decisions , data mining tools lighten the burden on specialiats in the medical field . Better treatment outcomes will arise from early diabetes prediction . In this scope, a publicly available diabetes dataset, which includes 16 features that are collected from 952 people, was used to create predictive models. I apply two machine learning algorithm such as Gradient Boost and AdaBoost . Python is used to train the suggested method, and an actual dataset obtained from Kaggle is used for analysis. Additionally, the confusion matrix and performance metrics are used to assess how well the suggested mechanism performs..The Gradient Boost model outperforms the other models, according to the comparison between the two..
Copyright
Copyright © 2024 S.Vijayalakshmi. This is an open access article distributed under the Creative Commons Attribution License.
