Name
Title | ||
|---|---|---|
Multi-model Short-term Prediction Schema for mHealth Empowering Asthma Self-management. |
Abstract | ||
|---|---|---|
Ambient intelligence and machine learning techniques are widely proposed by various eHealth and mHealth applications for home-care and self-management of various chronic health conditions. Their adoption for self-management of asthma, a multifactorial chronic disease, requires evaluation and validation in a real-life setups along with optimization at patient level to personalize predictions with respect to asthma control status and exacerbation risk. The current work proposes a novel short-term prediction approach for asthma control status, considering training of multiple classification models for each monitored parameters along with necessary pre-processing methods to enhance robustness and efficiency. The machine learning algorithms considered in this study are the Support Vector Machines, the Random Forests, AdaBoost and Bayesian Network. The Random Forests and Support Vector Machines classifiers demonstrated overall superior performance for the case studies (models) considered. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.entcs.2019.04.007 | Electronic Notes in Theoretical Computer Science |
Keywords | Field | DocType |
asthma control,personalized self-management,short-term prediction,machine learning algorithms,decision support system,mHealth | AdaBoost,Computer science,Ambient intelligence,Support vector machine,Robustness (computer science),Theoretical computer science,eHealth,mHealth,Bayesian network,Artificial intelligence,Random forest,Machine learning | Journal |
Volume | ISSN | Citations |
343 | 1571-0661 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Otilia Kocsis | 1 | 57 | 10.41 |
| Aris S. Lalos | 2 | 192 | 32.84 |
| Gerasimos Arvanitis | 3 | 9 | 6.21 |
| K. Moustakas | 4 | 285 | 58.02 |