Resumen
The exponential growth of medical data has generated the need to implement new techniques of information analysis that support decision making. The objective of this article is to identify the aggregated value that data mining models have in decision making in the information given by exploratory analysis. It was used a case study methodology for two data sets, that look to determine the malignity of detected masses, in the breasts of patients, through the interpretation of attributes registered from the mases. The results show a complementary behavior of both techniques.
| Idioma original | Español |
|---|---|
| Publicación | Espacios |
| Volumen | 39 |
| N.º | 28 |
| Estado | Publicada - 1 jun 2018 |
Huella dactilar
Palabras clave
- Breast cancer
- Decision Trees
- K-means clustering
- Mammographic
Citar esto
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Análisis comparativo entre : «el análisis exploratorio de datos» y los modelos de «árboles de decisión» y «kmeans » en el diagnóstico de la malignidad en algunos exámenes de cáncer de mama. Un estudio de caso. / Sánchez Zuleta, Carmen Cecilia; Giraldo Marín, Lillyana María; Piedrahita Escobar, Carlos César; Bonet, Isis; Lochmüller, Christian; Tabares Betancur, Marta Silvia; Peña, Alejandro.
En: Espacios, Vol. 39, N.º 28, 01.06.2018.Resultado de la investigación: Contribución a una revista › Artículo
TY - JOUR
T1 - Análisis comparativo entre
T2 - «el análisis exploratorio de datos» y los modelos de «árboles de decisión» y «kmeans » en el diagnóstico de la malignidad en algunos exámenes de cáncer de mama. Un estudio de caso
AU - Sánchez Zuleta, Carmen Cecilia
AU - Giraldo Marín, Lillyana María
AU - Piedrahita Escobar, Carlos César
AU - Bonet, Isis
AU - Lochmüller, Christian
AU - Tabares Betancur, Marta Silvia
AU - Peña, Alejandro
PY - 2018/6/1
Y1 - 2018/6/1
N2 - The exponential growth of medical data has generated the need to implement new techniques of information analysis that support decision making. The objective of this article is to identify the aggregated value that data mining models have in decision making in the information given by exploratory analysis. It was used a case study methodology for two data sets, that look to determine the malignity of detected masses, in the breasts of patients, through the interpretation of attributes registered from the mases. The results show a complementary behavior of both techniques.
AB - The exponential growth of medical data has generated the need to implement new techniques of information analysis that support decision making. The objective of this article is to identify the aggregated value that data mining models have in decision making in the information given by exploratory analysis. It was used a case study methodology for two data sets, that look to determine the malignity of detected masses, in the breasts of patients, through the interpretation of attributes registered from the mases. The results show a complementary behavior of both techniques.
KW - Breast cancer
KW - Decision Trees
KW - K-means clustering
KW - Mammographic
UR - http://www.scopus.com/inward/record.url?scp=85049863886&partnerID=8YFLogxK
M3 - Artículo
AN - SCOPUS:85049863886
VL - 39
JO - Espacios
JF - Espacios
SN - 0798-1015
IS - 28
ER -