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http://hdl.handle.net/10609/139066
Title: | Machine learning in a DEXA database of HIV patients |
Author: | Piqué Villorbina, Jordi |
Tutor: | Perez-Alvarez, Nuria |
Others: | Ventura, Carles |
Abstract: | The work is based on a DEXA database with information on three diseases, Sarcopenia, Lipodystrophy and Osteoporosis. The information in the database is about patients with AIDS, a disease that today still has a high incidence in the population. The treatments have greatly improved the life expectancy of patients but have also increased the risk of having some of the three pathologies mentioned. The variables in the database are related to these three diseases. What will be done is a descriptive analysis of the database and a prediction of the diseases, but doing the prediction of one disease through the variables of the other two diseases. The prediction will be made with the best known Machine Learning(ML) algorithms and will be done categorically and numerically. It will also be considered whether the variable total bone mineral density is used to predict the level of osteoporosis. A dynamic report will be created with Rmarkdown that can be used to make predictions with other databases. |
Keywords: | DEXA HIV machine learning |
Document type: | info:eu-repo/semantics/masterThesis |
Issue Date: | Dec-2021 |
Publication license: | http://creativecommons.org/licenses/by-nc-nd/3.0/es |
Appears in Collections: | Bachelor thesis, research projects, etc. |
Files in This Item:
File | Description | Size | Format | |
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jpiqueviTFM1221memory.pdf | TFM memory | 3,63 MB | Adobe PDF | View/Open |
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