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Exceptional Model Mining: a logistic regression model on cancer registry data.

Andrea Benevenuta

Exceptional Model Mining: a logistic regression model on cancer registry data.

Rel. Tania Cerquitelli. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Matematica, 2020

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Abstract:

Finding interesting patterns in a cancer registry data can provide oncologists and medical experts with a new perspective to improve healthcare for cancer patients. In this paper, we apply a supervised local pattern mining called Exceptional Model Mining. Its aim is to find subgroups in the data that somehow behave differently from the norm. This behaviour is captured by a model and the interestingness of a subgroup is assessed according to a quality measure. In particular, we develop a logistic regression model and propose a few different quality measures based on statistical tests and probability distributions. Additionally, we provide a statistical test, the permutation test, to assess the interestingness of a subgroup from a statistical point of view. The results of the experiments show that the proposed model can retrieve some subgroups that may be of interest for doctors. Moreover, the results of the permutation test show that the most interesting subgroups are also statistically significant.

Relators: Tania Cerquitelli
Academic year: 2020/21
Publication type: Electronic
Number of Pages: 68
Subjects:
Corso di laurea: Corso di laurea magistrale in Ingegneria Matematica
Classe di laurea: New organization > Master science > LM-44 - MATHEMATICAL MODELLING FOR ENGINEERING
Ente in cotutela: TECHNISCHE UNIVERSITEIT EINDHOVEN - Department of Mathematics and Computer Science (PAESI BASSI)
Aziende collaboratrici: Eindhoven University of Technology TU/e
URI: http://webthesis.biblio.polito.it/id/eprint/16290
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