Ciu, Tania and Oetama, Raymond Sunardi (2020) Logistic Regression Prediction Model for Cardiovascular Disease. IJNMT (International Journal of New Media Technology), 7 (1). pp. 33-38. ISSN 2355-0082
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Abstract
It is undeniable that cardiovascular disease is the number one cause of death in the world. Various factors such as age, cholesterol level, and unhealthy lifestyle can trigger cardiovascular disease. The symptoms of cardiovascular disease are also challenging to identify. It takes careful understanding and analysis related to patient medical record data and identification of the parameters that cause this disease. This study was conducted to predict the main factors causing cardiovascular disease. In this study, a dataset consisting of 14 attributes with class labels was used as the basis for identification as a link between factors that cause cardiovascular disease. The research area used is the area of analysis data where the analyzed data are on factors that influence the presence of cardiovascular disease in the State of Cleveland. In predicting cardiovascular disease, a logistic regression algorithm will be used to see the interrelation between the dependent variable and the independent variables involved. With this research, it is expected to be able to increase readers' knowledge and insight related to how to analyze cardiovascular disease using logistic regression algorithms and the main factors that cause cardiovascular disease.
| Item Type: | Article |
|---|---|
| Keywords: | Decision Tree, K-Means Algorithm, Logistic Regression, Naïve Bayes |
| Subjects: | 000 Computer Science, Information and General Works > 000 Computer Science, Knowledge and Systems > 005 Computer Programming > 005.2 Programming for Specific Computers, Algorithm, HTML, PHP, java, C++ |
| Divisions: | Faculty of Engineering & Informatics > Information System |
| Depositing User: | Administrator UMN Library |
| Date Deposited: | 04 Apr 2023 06:46 |
| Last Modified: | 12 Jun 2024 03:05 |
| URI: | https://kc.umn.ac.id/id/eprint/25232 |
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