Features of using Cox regression in various instrumental environments
The presence of large amounts of data in information and analytical systems makes it necessary to study them using machine learning and artificial intelligence methods. These models require the definition of tuning parameters related to the specifics of the subject area. The article presents a Cox r...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Publishing House of the State University of Management
2022-11-01
|
| Series: | Вестник университета |
| Subjects: | |
| Online Access: | https://vestnik.guu.ru/jour/article/view/3885 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832541586291949568 |
|---|---|
| author | I. V. Kramarenko L. A. Konstantinova |
| author_facet | I. V. Kramarenko L. A. Konstantinova |
| author_sort | I. V. Kramarenko |
| collection | DOAJ |
| description | The presence of large amounts of data in information and analytical systems makes it necessary to study them using machine learning and artificial intelligence methods. These models require the definition of tuning parameters related to the specifics of the subject area. The article presents a Cox regression model to solve the problem of customer churn. Cox regression is recognized as a model with high accuracy of predictions in healthcare. Therefore, it is interesting to use the model in other industries. The paper presents the results and comparative analysis of calculations on the Cox model using three tools: Statistical Package for the Social Sciences, programming language R and Russian software – analytical platform Loginom. A distinctive feature of the developed probabilistic model is the determination of the risk of event occurrence in conditions of incomplete data, as well as the identification of indicators that have a significant impact on the degree of its manifestation. |
| format | Article |
| id | doaj-art-f1f751960d924a45ad0a188471069d82 |
| institution | Kabale University |
| issn | 1816-4277 2686-8415 |
| language | English |
| publishDate | 2022-11-01 |
| publisher | Publishing House of the State University of Management |
| record_format | Article |
| series | Вестник университета |
| spelling | doaj-art-f1f751960d924a45ad0a188471069d822025-02-04T08:28:14ZengPublishing House of the State University of ManagementВестник университета1816-42772686-84152022-11-01010808810.26425/1816-4277-2022-10-80-882597Features of using Cox regression in various instrumental environmentsI. V. Kramarenko0L. A. Konstantinova1State University of ManagementState University of ManagementThe presence of large amounts of data in information and analytical systems makes it necessary to study them using machine learning and artificial intelligence methods. These models require the definition of tuning parameters related to the specifics of the subject area. The article presents a Cox regression model to solve the problem of customer churn. Cox regression is recognized as a model with high accuracy of predictions in healthcare. Therefore, it is interesting to use the model in other industries. The paper presents the results and comparative analysis of calculations on the Cox model using three tools: Statistical Package for the Social Sciences, programming language R and Russian software – analytical platform Loginom. A distinctive feature of the developed probabilistic model is the determination of the risk of event occurrence in conditions of incomplete data, as well as the identification of indicators that have a significant impact on the degree of its manifestation.https://vestnik.guu.ru/jour/article/view/3885cox regressionriskcustomer loyalty managementanalytical platformstatistical package |
| spellingShingle | I. V. Kramarenko L. A. Konstantinova Features of using Cox regression in various instrumental environments Вестник университета cox regression risk customer loyalty management analytical platform statistical package |
| title | Features of using Cox regression in various instrumental environments |
| title_full | Features of using Cox regression in various instrumental environments |
| title_fullStr | Features of using Cox regression in various instrumental environments |
| title_full_unstemmed | Features of using Cox regression in various instrumental environments |
| title_short | Features of using Cox regression in various instrumental environments |
| title_sort | features of using cox regression in various instrumental environments |
| topic | cox regression risk customer loyalty management analytical platform statistical package |
| url | https://vestnik.guu.ru/jour/article/view/3885 |
| work_keys_str_mv | AT ivkramarenko featuresofusingcoxregressioninvariousinstrumentalenvironments AT lakonstantinova featuresofusingcoxregressioninvariousinstrumentalenvironments |