Relevance Vector Machine as Data-Driven Method for Medical Decision Making
| Autor: | J-C. Deharo, J-F. Pons, Youssef Trardi, Stephane Delliaux, Bouchra Ananou, Mustapha Ouladsine, Z. Haddi |
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| Přispěvatelé: | Laboratoire d'Informatique et Systèmes (LIS), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), WitMonki, Nutrition, obésité et risque thrombotique (NORT), Aix Marseille Université (AMU)-Institut National de la Recherche Agronomique (INRA)-Institut National de la Santé et de la Recherche Médicale (INSERM), Hôpital de la Timone [CHU - APHM] (TIMONE), University Naples Federico II, University Sannio Benevento, ANR-11-IDEX-0001,Amidex,INITIATIVE D'EXCELLENCE AIX MARSEILLE UNIVERSITE(2011), Institut National de la Recherche Agronomique (INRA)-Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM) |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
Multivariate statistics business.industry Computer science 020208 electrical & electronic engineering Univariate Cardiac arrhythmia 02 engineering and technology Machine learning computer.software_genre 3. Good health Data-driven Relevance vector machine 020901 industrial engineering & automation 0202 electrical engineering electronic engineering information engineering Artificial intelligence Time series Medical diagnosis business computer Classifier (UML) [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology |
| Zdroj: | IEEE Xplore Digital Library 18. EUROPEAN CONTROL CONFERENCE (ECC) 18. EUROPEAN CONTROL CONFERENCE (ECC), University Naples Federico II; University Sannio Benevento, Jun 2019, Naples, Italy. ⟨10.23919/ECC.2019.8796141⟩ ECC |
| Popis: | International audience; The aim of this work is to develop an efficient data-driven method for automatic medical decision making, especially for cardiac arrhythmia diagnosis. To achieve this goal, we have targeted the most common arrhythmia worldwide -atrial fibrillation (AF). Most of reported studies are dealing with inter-beat interval time series analysis coupled with univariate and/or multivariate data-driven methods. The state of the art of this subject revealed that although satisfactory detection findings have been achieved for long AF durations, there is still scope for improvement which needs to be addressed for brief episodes which is highly desired by healthcare professionals. Relevance vector machine (RVM) has been developed to address this issue. Several kernel functions and parameters have been tested to optimize RVM. Five geometrical and nonlinear features were extracted from 30s inter-beat time series. The RVM classifier was trained on 3000 randomly selected samples from four publicly-accessible sets of clinical data and tested on 1000 samples. The performance of the diagnosis model was evaluated by 10-fold cross-validation method. The results showed that the RVM model performed better than do existing algorithms, with 96.58% success rate. The automatic diagnosis on another dataset of 118985 samples of AF and Normal Sinus Rhythm (NSR) has yield 96.64% of classification accuracy. This automated data-driven decision making approach can be exploited for medical diagnosis of other arrhythmias. |
| Databáze: | OpenAIRE |
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