A comparative analysis between classification algorithms for recognizing the types of food ingested
| Autor: | Cynthia Moreira Maia, Christina Pacheco, Julio Cartier Maia Gomes, Cicilia Raquel Maia Leite, Otília de Sousa Santos, Patrício de Alencar Silva, Lenardo Chaves e Silva, Angélica Félix de Castro |
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| Rok vydání: | 2020 |
| Předmět: |
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020203 distributed computing Computer science business.industry Pattern recognition 02 engineering and technology Medical decision making Regression Cross-validation Random forest Support vector machine Statistical classification Naive Bayes classifier ComputingMethodologies_PATTERNRECOGNITION 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business |
| Zdroj: | EATIS |
| DOI: | 10.1145/3401895.3402074 |
| Popis: | This paper presents a comparative analysis of five classification algorithms: k-nearest neighbor (k-NN), Support Vector Machine (SVM), Classification and Regression Trees (CART), Naive Bayes (NB) and Random Forest (RF) for recognition of three types of food ingested: solid, liquid and pasty, given that the use of these algorithms can optimize and assist in medical decision making. To achieve this goal, data on mandibular movements from the food intake process and the chewing time of 23 volunteers were captured. This data was captured through a noninvasive device. For training and validation of the algorithms, leave-one-out cross validation was used. As a result, the SVM algorithm performed better with a final accuracy of 89.8 %. |
| Databáze: | OpenAIRE |
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