Different Supervised Machine Learning Methods For Predicting And Analyzing the Diabetes
DOI:
https://doi.org/10.5281/zenodo.6369551Keywords:
Supervised Machine Learning, Random Forest, Decision Tree, eXtreme Gradient Boosting, Support Vector MachineAbstract
Diabetes Mellitus is one of the critical essential illnesses
that influence masses of humans. Diabetes Mellitus affects how our
body uses blood sugar(glucose). It is caused due unregular exercise,
our lifestyle, our food habits, high blood pressure level, etc. Many
diabetes people have a high risk of different types of diseases such
as kidney problems, heart problems, stroke, etc. In the existing
system, classification and prediction accuracy is not at a high level.
In this paper, Glucose, BMI, blood pressure, Age, etc are the
external factors for the prediction of better classification of
diabetes. This is the classification technique of supervised system
getting to know. This is used to predict whether the patient is diabetes
or non- diabetes . There are several predictor variables and one
target variable referred to as outcome on the diagnostic
measurement on the given dataset. This paper discusses the various
applications for predicting and understanding diabetes among
people.