Finding Biomarkers for Type 2 Diabetes

Bachelor Thesis (2023)
Author(s)

A. Das (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Contributor(s)

T.E.P.M.F. Abeel – Mentor (TU Delft - Pattern Recognition and Bioinformatics)

E.A. van der Toorn – Mentor (TU Delft - Pattern Recognition and Bioinformatics)

D. Calderon Franco – Mentor (TU Delft - BT/Environmental Biotechnology)

T. Höllt – Graduation committee member (TU Delft - Computer Graphics and Visualisation)

Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright
© 2023 Aratrika Das
More Info
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Publication Year
2023
Language
English
Copyright
© 2023 Aratrika Das
Graduation Date
29-06-2023
Awarding Institution
Delft University of Technology
Project
CSE3000 Research Project
Programme
Computer Science and Engineering
Faculty
Electrical Engineering, Mathematics and Computer Science
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Abstract

Type 2 Diabetes is a very prevalent disease in current times and leads to significant adverse effects. Recently, there has been a growing interest in the association of the human gut microbiome with respect to chronic diseases like Type 2 Diabetes with the aim to identify biomarkers. In this study, we researched the effect of different machine learning and feature selection techniques to identify biomarkers for Type 2 Diabetes that can later be used for diagnosis and prediction. The main methods that we explored were Random Forests,Linear Regression, Support Vector Machines andXGBoost along with mRMR and CMIM as feature selection techniques. These methods were applied to data taken from Europe and China. We found that mRMR improved the performance of the Random Forest classifier compared to CMIM.Apart from finding biomarkers specific to one location, we found that Clostridiales, Clostridium, Roseburia and Lactobacillus could be of interestin the prediction of Type 2 Diabetes irrespective of location. This study verified biomarkers found in previous literature and evaluated several techniquesfor the prediction of the disease across different regions.

Files

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