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\begin{document}

\title{Application of concept Drift Detection and Adaptive Framework for Non
Linear Time Series Data from Cardiac Surgery}



\author[1]{Nitin Auluck}%
\author[2]{Rajarajan Ganesan}%
\author[1]{Tarunpreet Kaur}%
\author[2]{Alisha Mittal}%
\author[1]{Mansi Sahi}%
\author[2]{Sushant Konar}%
\author[2]{Tanvir Samra}%
\author[2]{Goverdhan Dutt Puri}%
\author[2]{Shayam Kumar Singh Thingnum}%
\affil[1]{Indian Institute of Technology Ropar}%
\affil[2]{Post Graduate Institute of Medical Education and Research}%


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  \date{June 08, 2024}


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\begin{abstract}
The quality of machine learning (ML) models deployed in dynamic
environments tends to decline over time due to disparities between the
data used for training and the upcoming data available for prediction,
which is commonly known as drift. Therefore, it is important for ML
models to be capable of detecting any changes or drift in the data
distribution and updating the ML model accordingly. This study presents
various drift detection techniques to identify drift in the survival
outcomes of patients who underwent cardiac surgery. Additionally, this
study proposes several drift adaptation strategies, such as adaptive
learning, incremental learning, and ensemble learning. Through a
detailed analysis of the results, the study confirms the superior
performance of ensemble model, achieving a minimum mean absolute error
(MAE) of 10.684 and 2.827 for predicting hospital stay and ICU stay,
respectively. Furthermore, the models that incorporate a drift adaptive
framework exhibit superior performance compared to the models that do
not include such a framework.%
\end{abstract}%



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