A novel intelligent approach for predicting atherosclerotic individuals from big data for healthcare
| Autor: | Paulraj Ranjith Kumar, Mohan Priya |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
Engineering
business.industry Strategy and Management Disease progression Big data Decision tree Disease Filter (signal processing) Management Science and Operations Research Machine learning computer.software_genre Industrial and Manufacturing Engineering Correlation Feature (computer vision) Principal component analysis Data mining Artificial intelligence business computer |
| Zdroj: | International Journal of Production Research. 53:7517-7532 |
| ISSN: | 1366-588X 0020-7543 |
| DOI: | 10.1080/00207543.2015.1087655 |
| Popis: | Atherosclerosis is a condition in human circulatory, where the arteries become narrowed and hardened due to accumulation of plaque around artery wall. The growth of the disease is slow and asymptomatic. Currently, imaging methods are applied for predicting the disease progression; however, they are deficient in the required resolution and sensitivity for detection. In this work, clinical observations and habits of individuals are considered for assorting the pathologic community. Intelligent machine learning technique, decision tree forest is used for assorting the individuals. A case study was made in this work regarding the atherosclerosis disease progression and crucial features were extracted. Optimised missing value imputation strategy, iterative principal component analysis for STULONG data-set and efficient feature subset selection method, hybrid fast correlation-based filter (FCBF) have been employed for extracting the relevant features and ignoring the redundant features. Further proceeding with ... |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
| Nepřihlášeným uživatelům se plný text nezobrazuje | K zobrazení výsledku je třeba se přihlásit. |