@inproceedings{1b5f79fd684e4f9baca93b5f01acc439,
title = "Predictive model based on machine learning for the detection of physically mistreated women in the Peruvian scope",
abstract = "Violence against women in recent years in Peru showed alarming results, where 65.0 % of them were victims of some type of violence at some time in their lives. The study consisted of the elaboration of a predictive model that allows the recognition of the future physically mistreated woman. The data used was from a public survey of national scope. The methodology specified the data obtained; three supervised learning models Random Forest Classifier, Decision Tree Classifier, and Extra Trees Classifier were developed with the intention of buying the results and selecting that of the best performance. Regarding the results, Random Forest Classifier was the best model to obtain a precision of 51.0 % and a recall of 40.0 %, above those obtained by the other algorithms used. Subsequently, the best model passed to a calibration process where the average score was 0.7182, the precision was 0.76, and the recall was 0.31; concluding that the model classifies 76.0% of cases as physically mistreated women of which 31.0 % are effectively mistreated.",
keywords = "Decision Tree, Machine learning, Physical violence, Predictive model, Random Forest",
author = "Nemias Saboya and {Angel Sullon}, A. and Loaiza, {Omar L.}",
note = "Publisher Copyright: {\textcopyright} 2019 ACM.; 3rd International Conference on Advances in Artificial Intelligence, ICAAI 2019 ; Conference date: 26-10-2019 Through 28-10-2019",
year = "2019",
month = oct,
day = "26",
doi = "10.1145/3369114.3369143",
language = "English",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery",
pages = "18--23",
booktitle = "ICAAI 2019 - 2019 the 3rd International Conference on Advances in Artificial Intelligence",
}