Nonclinical Features in Predictive Modeling of Cardiovascular Diseases: A Machine Learning Approach
Background In the broader healthcare domain, the prediction bears more value than an explanation considering the cost of delays in its services. There are various risk prediction models for cardiovascular diseases (CVDs) in the literature for early risk assessment. However, the substantial increase...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2021
|
| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/31891/1/Nonclinical%20Features%20in%C2%A0Predictive%20Modeling.pdf http://umpir.ump.edu.my/id/eprint/31891/ https://doi.org/10.1007/s12539-021-00423-w https://doi.org/10.1007/s12539-021-00423-w |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Background
In the broader healthcare domain, the prediction bears more value than an explanation considering the cost of delays in its services. There are various risk prediction models for cardiovascular diseases (CVDs) in the literature for early risk assessment. However, the substantial increase in CVDs-related mortality is challenging global health systems, especially in developing countries. This situation allows researchers to improve CVDs prediction models using new features and risk computing methods. This study aims to assess nonclinical features that can be easily available in any healthcare systems, in predicting CVDs using advanced and flexible machine learning (ML) algorithms. |
|---|