Compaative Analysis of Random Forest And Logistic Regression For Diagnosis Of Diabetes Mellitus
Date
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Department of Computer Science and Information Technology
Abstract
In our daily life there is lots of data in different field. Whenever there is data we can
have lots of information, patterns, meaning etc. and the process of Extracting or
“mining” knowledge from large amount of data is called Data mining and is also
known as “Knowledge discovery from data (KDD)”. Data mining applications has got
rich focus due to its significance of classification algorithms. Diabetes Mellitus (DM)
is a result of bad metabolism. DM, if not controlled, causes several complications and
even affects other parts of the body. This study aims to survey on the two different
classifiers with data set of patients regarding Diabetes Mellitus and to implement as
well as assist by comparing Random Forest and Logistic Regression classification
techniques to standardize the diagnosis and treatment of Diabetes Mellitus. From the
context analysis it was seen that Logistic Regression was able to classify 81.17% of
the data correctly which was better than Random Forest in comparison to results of
evaluation metrics (Accuracy, Precision, Recall and F-Measure). In a nut shell, the
experiment result showed that Logistic Regression had got 2% better accuracy than
Random Forest for the diagnosis of diabetes mellitus.