Análisis de conglomerados de cáncer: el caso del barrio de Campdorà, Girona

Lertxundi, Aitana; Sáez, Marc; Marcos‐Gragera, Rafael; Izquierdo, Àngel; Pibernat, Nuri; Sala, E; Camps, Neus

doi:10.1590/s1135-57272005000400002

Search citation statements

Order By: Relevance

Paper Sections

Select...

Clustering Algorithm For Cancer Data1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2009

Publication Types

Select...

Book1

Article1

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

(1 citation statement)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A conglomerate of cases may be defined as the onset of a number of cases of a disease larger than expected for a certain population group, geographical area or timeframe. In the case of cancer, the study of conglomerates entails a number of specific characteristics as compared to other groups of diseases that will support new prevention plans to reduce the mortality rates or to outline new investigations (see [4]).…”

Section: Clustering Algorithm For Cancer Datamentioning

confidence: 99%

A Mixed Data Clustering Algorithm to Identify Population Patterns of Cancer Mortality in Hijuelas-Chile

Malo

Salas

Catalán

et al.

Artificial Intelligence in Medicine

View full text Add to dashboard Cite

The cancer disease in Hijuelas-Chile represents the 45% of the population deaths in the last decade. This high mortality rate have concerned the sanitary authority that lacks of information to identify the risk groups and the factors that influence in the disease.In this work we propose a clustering algorithm for mixed numerical, categorical and multi-valued attributes. We apply our proposed algorithm to identify and to characterize the common patterns in people who died of cancer in the population of Hijuelas between 1994 and 2006. As a consequence of this research, we were able to characterize the people who died of Cancer in Hijuelas-Chile.

show abstract

Section: Clustering Algorithm For Cancer Datamentioning

confidence: 99%