Thesis supervisor: László Szathmáry
Location of studies (in Hungarian): University of Debrecen Faculty of Informatics Abbreviation of location of studies: DE IK
Description of the research topic:
Cardiovascular disease (CVD) refers to any ailment that affects the heart or blood vessels. It is commonly connected with the formation of fatty deposits inside the arteries and an increased risk of blood clots. It has also been linked to artery damage in organs including as the brain, heart, kidneys, and eyes. Cardiovascular disease is the leading cause of mortality in the globe. Cardiac Magnetic Resonance Imaging (MRI) is regarded as the non-invasive gold standard for diagnosing cardiomyopathy.
Over the past decade, the research community has paid attention to predicting cardiovascular disease using machine learning classifiers. This includes using Support Vector Machine (SVM), Optimum Path Forest (OPF), random forest, multi-layer perceptron neural network (NN), K-Nearest Neighbors (KNN), Adaboost, naive bayes, decision trees and deep learning. The goal of the thesis is to investigate, implement and compare various machine learning methods for cardiovascular disease detection. Comparisons can be done using different performance evaluation metrics.
Bibliography
• Sorgel, W., & Vaerman, V. (1997). Automatic heart localization from a 4d MRI data set. Medical Imaging 1997: Image Processing, 3034, 333–344.
• Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., & Süsstrunk, S. (2012). SLIC superpixels compared to stateof-the-art superpixel methods. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(11), 2274–2282.
• LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.
• Salerno, M., Sharif, B., Arheden, H., Kumar, A., Axel, L., Li, D., & Neubauer, S. (2017). Recent advances in cardiovascular magnetic resonance techniques and applications. Circulation Cardiovascular Imaging, 10(6).
Deadline for application: 2024-05-15
2024. IV. 17. ODT ülés Az ODT következő ülésére 2024. június 14-én, pénteken 10.00 órakor kerül sor a Semmelweis Egyetem Szenátusi termében (Bp. Üllői út 26. I. emelet).
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