Academic Journal
Machine learning and physical based modeling for cardiac hypertrophy
| العنوان: | Machine learning and physical based modeling for cardiac hypertrophy |
|---|---|
| المؤلفون: | Bogdan Milićević, Miljan Milošević, Vladimir Simić, Andrej Preveden, Lazar Velicki, Đorđe Jakovljević, Zoran Bosnić, Matej Pičulin, Bojan Žunkovič, Miloš Kojić, Nenad Filipović |
| المصدر: | Heliyon, Vol 9, Iss 6, Pp e16724- (2023) |
| بيانات النشر: | Elsevier, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Science (General) LCC:Social sciences (General) |
| مصطلحات موضوعية: | Finite element analysis, Machine learning, Left ventricle mode, Cardiac hypertrophy, Disease progress tracking, Science (General), Q1-390, Social sciences (General), H1-99 |
| الوصف: | Background and objective: Predicting the long-term expansion and remodeling of the left ventricle in patients is challenging task but it has the potential to be clinically very useful. Methods: In our study, we present machine learning models based on random forests, gradient boosting, and neural networks, used to track cardiac hypertrophy. We collected data from multiple patients, and then the model was trained using the patient's medical history and present level of cardiac health. We also demonstrate a physical-based model, using the finite element procedure to simulate the development of cardiac hypertrophy. Results: Our models were used to forecast the evolution of hypertrophy over six years. The machine learning model and finite element model provided similar results. Conclusions: The finite element model is much slower, but it's more accurate compared to the machine learning model since it's based on physical laws guiding the hypertrophy process. On the other hand, the machine learning model is fast but the results can be less trustworthy in some cases. Both of our models, enable us to monitor the development of the disease. Because of its speed machine learning model is more likely to be used in clinical practice. Further improvements to our machine learning model could be achieved by collecting data from finite element simulations, adding them to the dataset, and retraining the model. This can result in a fast and more accurate model combining the advantages of physical-based and machine learning modeling. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2405-8440 |
| Relation: | http://www.sciencedirect.com/science/article/pii/S2405844023039312; https://doaj.org/toc/2405-8440 |
| DOI: | 10.1016/j.heliyon.2023.e16724 |
| URL الوصول: | https://doaj.org/article/3623697cb55743d699402dcdd5abfb8c |
| رقم الانضمام: | edsdoj.3623697cb55743d699402dcdd5abfb8c |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 24058440 |
|---|---|
| DOI: | 10.1016/j.heliyon.2023.e16724 |