Journal
JOURNAL OF MEDICAL SYSTEMS
Volume 40, Issue 8, Pages -Publisher
SPRINGER
DOI: 10.1007/s10916-016-0548-8
Keywords
Machine learning; Natural language processing; Obesity; Comorbidities
Funding
- informatics division of the Hospital Guillermo Grant Benavente (HGGB)
- Universidad de Concepcion
- Fondo Nacional de Desarrollo Cientifico y Tecnologico (FONDECYT) [11121463]
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Obesity is a chronic disease with an increasing impact on the world's population. In this work, we present a method of identifying obesity automatically using text mining techniques and information related to body weight measures and obesity comorbidities. We used a dataset of 3015 de-identified medical records that contain labels for two classification problems. The first classification problem distinguishes between obesity, overweight, normal weight, and underweight. The second classification problem differentiates between obesity types: super obesity, morbid obesity, severe obesity and moderate obesity. We used a Bag of Words approach to represent the records together with unigram and bigram representations of the features. We implemented two approaches: a hierarchical method and a nonhierarchical one. We used Support Vector Machine and Naive Bayes together with ten-fold cross validation to evaluate and compare performances. Our results indicate that the hierarchical approach does not work as well as the nonhierarchical one. In general, our results show that Support Vector Machine obtains better performances than Naive Bayes for both classification problems. We also observed that bigram representation improves performance compared with unigram representation.
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