A Support Vector Machine Approach for Categorization of Patients Suffering from Chronic Diseases
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Recent works in diagnosis/decision making involve estimating severity of health episodes of patients with chronic disease, sleep issues such as polysomnography and apnea, estimation and classification of health conditions, and emotion recognition.
“Some examples of recent works in this area involve estimating the severity of health episodes of patients suffering chronic disease [44–46], sleep issues such as polysomnography and apnea [47,48], estimation and classification of health conditions [49,50], and emotion recognition [51].”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [44]
Several binary SVMs with different kernels such as RBF, polynomial, and sigmoid were combined to model input data from multi-sensor features based on expert labels on data episodes (4 levels of severity).
“Based on the expert's labels on data episodes (4 levels of the severity), several binary SVMs with different kernels such as RBF, polynomial, and sigmoid are combined to model the input data from multi sensor features.”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [44]
A one-against-all SVMs method was used to handle multi-label classification for detecting patient's condition.
“Detection of the patient's condition is the topic of [44] which used a one-against-all SVMs method in order to handle multi-label classification.”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [44]
Some research has been done on unlabeled data to consider uncontrolled situations, especially for experimental data sets.
“Some researches have been done on unlabeled data in order to consider uncontrolled situations for especially experimental data sets [39,44,88].”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [44]
Most research has used several sensors to have global reasoning in health monitoring.
“Besides the works focusing on physiological data mining gathered from a single sensor, most of the researches have used several sensors [30,40,44,67] to have global reasoning in health monitoring.”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [44]
Cites
A comparison of methods for multiclass support vector machines
Hsu, C., & Lin, C. (2002) · IEEE Transactions on Neural Networks · doi:10.1109/72.991427 · Cited by 6,861 · see citing works
A comparative study of differential evolution variants for global optimization
Mezura‐Montes, E., Velázquez-Reyes, J., & Coello, C. A. C. (2006) · Genetic and Evolutionary Computation Conference (GECCO) · doi:10.1145/1143997.1144086 · Cited by 602 · see citing works
Automatic model selection for the optimization of SVM kernels
Ayat, N. E., Cheriet, M., & Suen, C. Y. (2005) · Pattern Recognition · doi:10.1016/j.patcog.2005.03.011 · Cited by 184 · see citing works
Reduced one-against-all method for multiclass SVM classification
Kumar, M., & Gopal, M. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.04.237 · Cited by 49 · see citing works
Data Mining and Knowledge Discovery Technologies
Taniar, D. (2008) · IGI Global eBooks · doi:10.4018/978-1-59904-960-1 · Cited by 39 · see citing works
Extraction and Analysis of features acquired by wearable sensors network
Bellos, C. C., Papadopoulos, A., Rosso, R., & Fotiadis, D. I. (2010) · Proceedings of the 10th IEEE International Conference on Information Technology and Applications in Biomedicine · doi:10.1109/itab.2010.5687761 · Cited by 21 · see citing works
Self-citationCHRONIOUS: A wearable system for the management of chronic disease patients
Papadopoulos, A. N., Fotiadis, D. I., Lawo, M., et al. (2009) · doi:10.1109/itab.2009.5394341 · Cited by 16 · see citing works
Self-citationAn intelligent system for classification of patients suffering from chronic diseases
Bellos, C. V., Papadopoulos, A. N., Fotiadis, D. I., et al. (2010) · Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society · doi:10.1109/iembs.2010.5626314 · Cited by 12 · see citing works
Self-citationAn Intelligent System for Classification of Patients Suffering from Chronic Diseases
Bellos, C. V., Papadopoulos, A. N., Fotiadis, D. I., et al. (2012) · Wireless Mobile Communication and Healthcare · Cited by 4 · see citing works
Self-citation
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Banaee, H., Ahmed, M., & Loutfi, A. (2013) · Sensors · doi:10.3390/s131217472 · Cited by 452 · see citing works
- Authors
- Bellos, C., Papadopoulos, A., Rosso, R., & Fotiadis, D. I.
- Affiliations
- Foundation for Research and Technology Hellas (GR)
- Venue
- Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
- Publisher
- Springer Berlin Heidelberg
- Published
- 2012
- Pages
- 264-267
- ISSN
- 1867-8211 (ISSN-L)
- 1867-822X
- OpenAlex
- W103700182
- Cited by
- 5
- References
- 9
- Citations by year
- 2022: 1 · 2021: 1 · 2018: 1 · 2017: 1 · 2013: 1
- FWCI
- 0.60
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering (publishedVersion)
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Health Professions › Health Information Management › Innovation in Digital Healthcare Systems
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Keywords
- chronic disease management, chronic obstructive pulmonary disease, support vector machine, decision support system, wireless body area networks, vital signs, personal digital assistants, multi-class classification
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