2026-04-13T01:41:47Zhttps://recercat.cat/oai/requestoai:recercat.cat:2117/1019102025-07-22T17:35:57Zcom_2072_1033col_2072_452951
RECERCAT
author
Carrio Viladrich, Laura
2016
Machine learning and data mining methods can be the future of the clinical decision
process like pathological diagnosis. In this project we studied Breast Cancer Wisconsin
dataset and applied different algorithms, concretely classifiers, in order to predict the
diagnosis and the prognostic of the cancer.
In order to classify the different types of cancer we divided the classification in two steps
and we tested different algorithms for each step. The first step is the diagnosis
classification. Diagnosis consistsin predict if the cancer is malignant and benign. And the
second step is the prognostic classification. Prognostic consist in predict if cancer is
recurrent or non-recurrent.
After applying different models for each steps the result is that the best model to predict
the diagnosis is the Decision Forest model. And the best model to predict the prognostic
is the Boosted Decision Tree model.
So, we conclude that the two step classifier with Decision Forest model and Boosted
Decision Tree model is the best classifier.
Open Access
Àrees temàtiques de la UPC::Informàtica
Machine learning
Databases
Aprenentatge automàtic
Bases de dades
Data mining in Breast Cancer
Bachelor thesis