Research Article
Year 2019,
Volume: 2 Issue: 1, 15 - 21, 30.06.2019
Abstract
References
- 1. Aswathikutty, A., Marcenes, W., Stansfeld, S. A., & Bernabé, E. (2017). Obesity, physical activity and traumatic dental injuries in adolescents from East London. Dental traumatology, 33(2), 137-142.
- 2. Bookman, J. S., Schwarzkopf, R., Rathod, P., Iorio, R., & Deshmukh, A. J. (2018). Obesity: the modifiable risk factor in total joint arthroplasty. Orthopedic Clinics of North America.
- 3. Cirulli, E. T., Guo, L., Swisher, C. L., Shah, N., Huang, L., Napier, L. A., ... & Venter, J. C. (2018). Profound perturbation of the human metabolome by obesity. bioRxiv, 298224.
- 4. Cui, S., Zhao, L., Wang, Y., Dong, Q., Ma, J., Wang, Y., ... & Ma, X. (2018). Using Naive Bayes Classifier to predict osteonecrosis of the femoral head with cannulated screw fixation. Injury, 49(10), 1865-1870.
- 5. Efe A. (2012). Evaluation of obesity risk factors using logistic regression and artificial neural networks. Dokuz Eylül University, Master Thesis in Statistics.
- 6. Elmas Ç. (2003). Yapay Sinir Ağları. Seçkin Yayıncılık.
- 7. Ferenci, T., & Kovács, L. (2018). Predicting body fat percentage from anthropometric and laboratory measurements using artificial neural networks. Applied Soft Computing, 67, 834-839.
- 8. Hastie T., Tibshirani R. & Friedman J. (2008). The Elements of Statistical Learning. Second Edition. NY: Springer.
- 9. Hosmer D. & Lemeshow S. (1989). Applied logistic regression. NY: John Wiley&Sons.
- 10. Marques CDF., Silva RCR., Machado MEC., de Santana MLP., Cairo RCA., de Jesus Pinto E., et al.(2013). The prevalence of overweight and obesity in adolescents in Bahia, Brazil. Nutricion Hospitalaria, 28(2): 491-496.
- 11. Mbakwa, C. A., Hermes, G. D., Penders, J., Savelkoul, P. H., Thijs, C., Dagnelie, P. C.& Arts, I. C. (2018). Gut Microbiota and Body Weight in School‐Aged Children: The KOALA Birth Cohort Study. Obesity.
- 12. Orphanou, K., Dagliati, A., Sacchi, L., Stassopoulou, A., Keravnou, E., & Bellazzi, R. (2018). Incorporating repeating temporal association rules in Naïve Bayes classifiers for coronary heart disease diagnosis. Journal of biomedical informatics, 81, 74-82.
- 13. Sperandei, S. (2014). Understanding logistic regression analysis. Biochemia medica: Biochemia medica, 24(1), 12-18.
- 14. Wang, H., & Zhang, Y. (2016). Detection of motor imagery EEG signals employing Naïve Bayes based learning process. Measurement, 86, 148-158.
- 15. Yıldırım S. & Uskun E. (2018). Risk factors affecting obesity development in high school students: a community based case-control study. Türk Pediatri Ars; 53(3): 155-62.
- 16. Zhang, H., Cao, Z. X., Li, M., Li, Y. Z., & Peng, C. (2016). Novel naive Bayes classification models for predicting the carcinogenicity of chemicals. Food and Chemical Toxicology, 97, 141-149.
Year 2019,
Volume: 2 Issue: 1, 15 - 21, 30.06.2019
In the
artificial neural network, backward propagation learning algorithm was used as
the learning rule in the adjustment of the connection weights according to the
output. With the Naive Bayes method, a classification based on the probability
values of the data was performed. The logistic regression model coefficient
values were determined, using the maximum likelihood method. According to
obesity questionnaire data, it was determined whether the relationship of each
obesity risk factor with the response variable was statistically significant.
The Naive Bayes method has the highest accuracy in prediction obesity compared
to the other two methods.
Abstract
Obesity, which negatively affects human health, is a
chronic disease due to genetic and living conditions. In this study, it was
aimed to examine the observations with three main techniques: logistic
regression, artificial neural networks and Naive Bayes, where the response
variable was two categories of obese/not obese. Obesity questionnaire data,
that was answered by 504 senior students in three randomly selected high
schools in Gaziemir, Izmir, were analysed, and the predictive competences of
the results of the three methods were evaluated. It was found that obesity is
affected by the mother and father’s being obese and eating too much fruit. In
addition, gender and diet status were significantly related with the obesity
risk.
In the
artificial neural network, backward propagation learning algorithm was used as
the learning rule in the adjustment of the connection weights according to the
output. With the Naive Bayes method, a classification based on the probability
values of the data was performed. The logistic regression model coefficient
values were determined, using the maximum likelihood method. According to
obesity questionnaire data, it was determined whether the relationship of each
obesity risk factor with the response variable was statistically significant.
The Naive Bayes method has the highest accuracy in prediction obesity compared
to the other two methods.
References
- 1. Aswathikutty, A., Marcenes, W., Stansfeld, S. A., & Bernabé, E. (2017). Obesity, physical activity and traumatic dental injuries in adolescents from East London. Dental traumatology, 33(2), 137-142.
- 2. Bookman, J. S., Schwarzkopf, R., Rathod, P., Iorio, R., & Deshmukh, A. J. (2018). Obesity: the modifiable risk factor in total joint arthroplasty. Orthopedic Clinics of North America.
- 3. Cirulli, E. T., Guo, L., Swisher, C. L., Shah, N., Huang, L., Napier, L. A., ... & Venter, J. C. (2018). Profound perturbation of the human metabolome by obesity. bioRxiv, 298224.
- 4. Cui, S., Zhao, L., Wang, Y., Dong, Q., Ma, J., Wang, Y., ... & Ma, X. (2018). Using Naive Bayes Classifier to predict osteonecrosis of the femoral head with cannulated screw fixation. Injury, 49(10), 1865-1870.
- 5. Efe A. (2012). Evaluation of obesity risk factors using logistic regression and artificial neural networks. Dokuz Eylül University, Master Thesis in Statistics.
- 6. Elmas Ç. (2003). Yapay Sinir Ağları. Seçkin Yayıncılık.
- 7. Ferenci, T., & Kovács, L. (2018). Predicting body fat percentage from anthropometric and laboratory measurements using artificial neural networks. Applied Soft Computing, 67, 834-839.
- 8. Hastie T., Tibshirani R. & Friedman J. (2008). The Elements of Statistical Learning. Second Edition. NY: Springer.
- 9. Hosmer D. & Lemeshow S. (1989). Applied logistic regression. NY: John Wiley&Sons.
- 10. Marques CDF., Silva RCR., Machado MEC., de Santana MLP., Cairo RCA., de Jesus Pinto E., et al.(2013). The prevalence of overweight and obesity in adolescents in Bahia, Brazil. Nutricion Hospitalaria, 28(2): 491-496.
- 11. Mbakwa, C. A., Hermes, G. D., Penders, J., Savelkoul, P. H., Thijs, C., Dagnelie, P. C.& Arts, I. C. (2018). Gut Microbiota and Body Weight in School‐Aged Children: The KOALA Birth Cohort Study. Obesity.
- 12. Orphanou, K., Dagliati, A., Sacchi, L., Stassopoulou, A., Keravnou, E., & Bellazzi, R. (2018). Incorporating repeating temporal association rules in Naïve Bayes classifiers for coronary heart disease diagnosis. Journal of biomedical informatics, 81, 74-82.
- 13. Sperandei, S. (2014). Understanding logistic regression analysis. Biochemia medica: Biochemia medica, 24(1), 12-18.
- 14. Wang, H., & Zhang, Y. (2016). Detection of motor imagery EEG signals employing Naïve Bayes based learning process. Measurement, 86, 148-158.
- 15. Yıldırım S. & Uskun E. (2018). Risk factors affecting obesity development in high school students: a community based case-control study. Türk Pediatri Ars; 53(3): 155-62.
- 16. Zhang, H., Cao, Z. X., Li, M., Li, Y. Z., & Peng, C. (2016). Novel naive Bayes classification models for predicting the carcinogenicity of chemicals. Food and Chemical Toxicology, 97, 141-149.
There are 16 citations in total.
Details
| Primary Language | English |
|---|---|
| Journal Section | Articles |
| Authors | |
| Publication Date | June 30, 2019 |
| Submission Date | November 28, 2018 |
| Acceptance Date | February 27, 2019 |
| Published in Issue | Year 2019 Volume: 2 Issue: 1 |
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