Please use this identifier to cite or link to this item:

http://hdl.handle.net/10609/90987
Title: Predictor para el síndrome de Lynch: comparativa y análisis de algoritmos de machine learning
Author: Muñoz López, Marta
Director: Andrio Balado, Pau
Keywords: Lynch syndrome
scikits.learn
Python
machine learning
Issue Date: Sep-2018
Publisher: Universitat Oberta de Catalunya (UOC)
Abstract: Along the development of this project, Lynch Syndrome has been analysed, an hereditarian syndrome which increases the possibilities of suffering different kinds of cancer. This syndrome is detected due to mutations in different genes like: MLH1, MSH1, MSH2, MSH6 o PMS2. A predictor has been developed coming from individual characteristics. First of all, data have been collected in order to apply to different machine learning algorithms. After finding the most suitable data, an analysis has been done. After that, different classification algorithms have been studied with the purpose of comparing all of them. The main library used in the product development has been Python scikit-learn. Some algorithms have been searched of in detail to obtain optima results. The project has been determined because of a small sample data.
Language: Spanish
URI: http://hdl.handle.net/10609/90987
Appears in Collections:Bachelor thesis, research projects, etc.

Share:
Export:
Files in This Item:
File Description SizeFormat 
mmunozlopez2TFM0119memoria.pdfMemoria del TFM1.07 MBAdobe PDFView/Open

This item is licensed under a Creative Commons License Creative Commons