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\begin{document}

\title{Comparison of Machine Learning Techniques for Precision in measurement
of glucose level in Artificial Pancreas}



\author[1]{* Nilam}%
\author[1]{Vijay K. Yadav}%
\affil[1]{Delhi Technological University Department of Applied Mathematics}%


\vspace{-1em}



  
  \date{May 26, 2024}


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\begin{abstract}
Precision in measurement of glucose level in artificial pancreas is a
challenging task and mandatory requirement for the proper functioning of
artificial pancreas. A suitable machine learning technique for the
measurement of glucose level in artificial pancreas may play crucial
role in the management of diabetes. Therefore in the present work, a
comparison has been made among few machine learning (ML) techniques for
measurement of glucose levels in artificial pancreas because the machine
learning is an astounding technology of artificial intelligence, and
widely applicable in various fields such as medical science, robotics,
environmental science, etc. The models namely decision tree (DT), random
forest (RF), support vector machine (SVM), and K-nearest neighbours
(KNN), based on supervised learning, are proposed for the dataset of
Pima Indian to predict and classify the diabetes mellitus. Ensuring the
predictions and accuracy up to the level of DMT2, the comparative
behavior of all four models has been discussed. The machine learning
models developed here stratifies and predicts whether an individual is
diabetic or not based on the features available in the data set. Dataset
passes through pre-processing and machine learning algorithms are fitted
to train the dataset, and then the performance of the test results has
been discussed. Error matrix (EM) has been generated to measure the
accuracy score of the models. The accuracies in prediction and
classification of DMT2 models are 71\%, 77\%, 78\%, and 80\% for DT,
SVM, RF and KNN algorithms respectively. The KNN model has shown a more
precise result in comparison to other models. The proposed methods have
shown astounding behaviour in terms of accuracy in the prediction of
diabetes mellitus as compared to previously developed methods.%
\end{abstract}%



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\textbf{Hosted file}

\verb`Nilam_AI_Manuscripts.docx`  available at \url{https://authorea.com/users/513576/articles/589714-comparison-of-machine-learning-techniques-for-precision-in-measurement-of-glucose-level-in-artificial-pancreas}

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