Resumen
In the present work, the CRISP-DM methodology was proposed to develop a set of machine learning models applied
to evaluate cervical cancer risk suffer. For this research, a sample of 858 patients was taken, who were asked a series
of questions regarding this pathology. The database has an unbalanced dependent variable, since this is a health study,
the balancing technique will not be used to identify the variables that will enter the model, the Boruta library was used
for variable selection. For the development, five algorithms will be used: Support Vector Machine (SVM), decision
trees using the CHAID and CART algorithms, logistic regression and "asymmetric link" models. The models proposed
in this work were refined by means of the Auc, Gini, Log loss and KS (Kolmogorov-Smirnov) indicators, as a result
using the proposed models, AUC values of 98% were obtained.
to evaluate cervical cancer risk suffer. For this research, a sample of 858 patients was taken, who were asked a series
of questions regarding this pathology. The database has an unbalanced dependent variable, since this is a health study,
the balancing technique will not be used to identify the variables that will enter the model, the Boruta library was used
for variable selection. For the development, five algorithms will be used: Support Vector Machine (SVM), decision
trees using the CHAID and CART algorithms, logistic regression and "asymmetric link" models. The models proposed
in this work were refined by means of the Auc, Gini, Log loss and KS (Kolmogorov-Smirnov) indicators, as a result
using the proposed models, AUC values of 98% were obtained.
| Título traducido de la contribución | Modelos de clasificación logística desbalanceada aplicada a la detección de cancer cervical. |
|---|---|
| Idioma original | Inglés |
| Título de la publicación alojada | Biased logistic models applied to cervical cancer risk |
| Lugar de publicación | United States |
| Editorial | IEOM Society International |
| Capítulo | 1 |
| Número de páginas | 7 |
| ISBN (versión digital) | 978-1-7923-9159-0 |
| Estado | Publicada - 28 nov. 2022 |
| Evento | Proceedings of the 3rd South American International Industrial Engineering and Operations Management Conference, Asuncion, Paraguay, July 19-21, 2022 - Universidad Nacional de Asunción - Paraguay, Asunción, Paraguay Duración: 19 jul. 2022 → 21 jul. 2022 Número de conferencia: 3 http://ieomsociety.org/paraguay2022/proceedings/ |
Conferencia
| Conferencia | Proceedings of the 3rd South American International Industrial Engineering and Operations Management Conference, Asuncion, Paraguay, July 19-21, 2022 |
|---|---|
| Título abreviado | IEOM Paraguay 2022 |
| País/Territorio | Paraguay |
| Ciudad | Asunción |
| Período | 19/07/22 → 21/07/22 |
| Dirección de internet |
Palabras Clave
- Machine learning
- Asymmetric linkage models
- Predictive models
Categoría OCDE
- Otras ingenierías y tecnologías
Categorías Repositorio Ulima
- Ingeniería de sistemas / Sistemas de información
ODS de las Naciones Unidas
Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible
