International Journal of Applied Science and Engineering
Published by Chaoyang University of Technology

Pineda-Rico Zaira 1*, Rojas Mendoza Diana Luz de los Angeles 1, Pineda-Rico Ulises 2, Arguelles Ojeda Jose Luis 1, Martinez Lopez Francisco Javier 1

1 Coordinacion Academica Region Altiplano, Universidad Autonoma de San Luis Potosi, Matehuala, S.L.P, 78700, Mexico

2 Facultad de Ciencias, Universidad Autonoma de San Luis Potosi., Privadas del Pedregal, 78295, Mexico

 

Download Citation: |
Download PDF


ABSTRACT

The detection of individuals with obesity or overweight allows to predict the prevalence of health risks, such as premature death, disabilities and other chronic diseases. This study describes a pilot conducted on the members of a higher education staff in the city of Matehuala, Mexico. It involved processing anthropometric measurements, health indicators and the results of bioelectrical impedance analysis using machine learning techniques. The goal was to identify the metabolic aging of individuals. The recorded data were used to create a database that was subsequently employed in four different classification models: decision tree, random forest, artificial neural networks and adaptive boosting. Additionally, four statistical techniques were utilized to determine variable importance scores: Pearson, Chi2, Anova, recursive elimination method and the variance inflation factor. The variable importance score was employed to identify the features that were most consistently repeated across methods. This analysis concluded that both anthropometric measurements and the results of bioelectrical impedance analysis provide valuable references for identifying obesity and overweight in individuals. Among the anthropometric measurements that exhibited a greater impact on the models' predictions were waist-to-height ratio, hip and arm circumferences, body mass index, systolic and diastolic blood pressure and heart rate. Additionally, body fat and muscle mass also contributed significantly.


Keywords: Classification, Machine learning, Obesity prediction, Variable importance.


Share this article with your colleagues

 


REFERENCES

  1. Aldobali, M., Pal, K., Chhabra, H. 2022. Noninvasive health monitoring using bioelectrical impedance analysis. Computational Intelligence in Healthcare Applications, 209–236.

  2. Archer, K.J., Kimes, R.V. 2008. Empirical characterization of random forest variable importance measures. Computational Statistics and Data Analysis, 52, 2249–2260.

  3. Ashwell, M., Gunn, P., Gibson, S. 2011. Waist-to-height ratio is a better screening tool than waist circumference and BMI for adult cardiometabolic risk factors: Systematic review and meta-analysis. Obesity Reviews, 13, 275–286.

  4. Barquera, S., Hernandez-Barrera, L., Trejo-Valdivia, B., Shamah, T., Campos-Nonato, I., Rivera-Dommarco, J. 2020. Obesidad en México, prevalencia y tendencias en adultos. Ensanut 2018-19. Salud Pública de México, 62, 682–692.

  5. Bohm, A., Heitmann, B.L. 2013. The use of bioelectrical impedance analysis for body composition in epidemiological studies. European Journal of Clinical Nutrition, 67, 79–85.

  6. Chen, R.C., Dewi, C., Huang, S.W., Caraka, R.E. 2020. Selecting critical features for data classification based on machine learning methods. Journal of Big Data, 7, 1–26.

  7. Crowson, M.G., Moukheiber, D., Arevalo, A.R., Lam, B.D., Mantena, S., Rana, A., Goss, D., Bates, D.W., Celi, L. A. 2022. A systematic review of federated learning applications for biomedical data. PLOS Digital Health, 1, 1–14.

  8. Chatterjee, A., Gerdes, M.W., Martinez, S.G. 2020. Identification of risk factors associated with obesity and overweight-a machine learning overview. Sensors (Basel), 20, 2734.

  9. de-Mateo-Silleras, B., de-la-Cruz-Marcos, S., Alonso-Izquierdo, L., Camina-Martín, M.A., Marugán-de-Miguelsanz, J.M., Redondo-Del-Río, M.P. 2019. Bioelectrical impedance vector analysis in obese and overweight children. PLoS One. 14, e0211148.

  10. Devajit, M., Haradhan, K.M. 2023. Body mass index (BMI) is a popular anthropometric tool to measure obesity among adults. Journal of Innovations in Medical Research, 2, 25–33.

  11. Dhabarde, S., Mahajan, R., Mishra, S., Chaudhari, S., Manelu, S., Shelke, N.S. 2022. Disease prediction using machine learning algorithms. 10th International Conference on Emerging Trends in Engineering and Technology-Signal and Information Processing (ICETET-SIP-22), 1–4.

  12. Freedman, M.R., Rubinstein, R.J. 2010. Obesity and food choices among faculty and staff at a large urban university. Journal of American College Health. 59, 205–210.

  13. Ganggayah, M.D., Taib, N.A., Har, Y.C., Lio, P., Dhillon, S.K. 2019. Predicting factors for survival of breast cancer patients using machine learning techniques. BMC Medical Informatics and Decision Making, 19, 1–17.

  14. Geron, A. 2019. Hands-on machine learning with scikit-learn and TensorFlow. O'Reilly Media. U.S.A.

  15. Gregorutti, B., Michel, B., Saint-Pierre, P. 2017. Correlation and variable importance in random forests. Statistics and Computing, 27, 659–678.

  16. Gutierrez-Esparza, G.O., Vazquez, O.I., Vallejo, M., Hernandez-Torruco, J. 2020. Prediction of metabolic syndrome in a Mexican population applying machine learning algorithms. Symmetry, 12. 581–596.

  17. He, L., Ren, X., Qian, Y., Jin, Y., Chen, Y., Guo, D., Yao, Y. 2014. Prevalence of overweight and obesity among a university faculty and staffs from 2004 to 2010, China. Nutrición Hospitalaria, 29, 1033–1037.

  18. Heydari, S.T., Ayatollahi, S.M., Zare, N. 2011. Diagnostic value of bioelectrical impedance analysis versus body mass index for detection of obesity among students. Asian Journal of Sports Medicine. 2, 68–74.

  19. Javaid, M., Haleem, A., Singh, R.P., Suman, R., Rab, S. 2022. Significance of machine learning in healthcare: Features, pillars and applications. International Journal of Intelligent Networks, 3, 58–73.

  20. Jensen, M.D. 2008. Role of body fat distribution and the metabolic complications of obesity. Journal of Clinical Endocrinology and Metabolism, 93, 57–63.

  21. Jiang, M., Yin, S. 2023. Facial expression recognition based on convolutional block attention module and multi-feature fusion. International Journal of Computational Vision and Robotics. 13, 21–37.

  22. Jiang, Y., Yin, S. 2023. Heterogenous-view occluded expression data recognition based on cycle-consistent adversarial network and K-SVD dictionary learning under intelligent cooperative robot environment. Computer Science and Information Systems, 34.

  23. Kiang, M.Y. 2003. A comparative assessment of classification methods. Decision Support Systems, 35, 441–454.

  24. Laghari, A.A., Yin, S. 2022. How to collect and interpret medical pictures captured in highly challenging environments that range from nanoscale to hyperspectral imaging. Current Medical Imaging, 54, 36582065.

  25. Laghari, A.A., He, H., Shafiq, M., Khan, A. 2018. Assessment of quality of experience (QoE) of image compression in social cloud computing. Multiagent Grid Systems, 14, 125–143.

  26. Laghari, A. A., Shahid, S., Yadav, R., Karim, S., Khan, A., Li, H., Yin, S. 2023. The state of art and review on video streaming. Journal of High Speed Networks, (Preprint), 1–26.

  27. Macias, N., Espinosa-Montero, J., Monterrubio-Flores, E., Hernandez-Barrera, L., Medina-Garcia, C., Gallegos-Carrillo, K. 2021. Screen-based sedentary behaviors and their association with Metabolic Syndrome components among adults in Mexico. Preventing Chronic Disease, 18, 1–12. 

  28. Manickam, P., Mariappan, S.A., Murugesan, S.M., Hansda, S., Kaushik, A., Shinde, R., Thipperudraswamy, S.P. 2022. Artificial intelligence (AI) and internet of medical things (IoMT) assisted biomedical systems for intelligent healthcare. Biosensors, 12, 562–591.

  29. McLaren, C.E., Chen, W.P., Nie, K., Su, M.Y. 2009. Prediction of malignant breast lesions from MRI features: A comparison of artificial neural network and logistic regression Techniques. Academic Radiology, 16, 842–851.

  30. Medina, C., Janssen, I., Campos, I., Barquera, S. 2013. Physical inactivity prevalence and trends among Mexican adults: Results from the national health and nutrition survey (ENSANUT) 2006 and 2012. BMC Public Health, 13, 1–10.

  31. Medina, C., Tolentino-Mayo, L., Lopez-Ridaura, R., Barquera, S. 2017.  Evidence of increasing sedentarism in Mexico City during the last decade: Sitting time prevalence, trends, and associations with obesity and diabetes. Plos One, 12, 1–15.

  32. Mendoza-Niño, C., Martinez-Robles, J.D., Gallardo-Garcia, I. 2023. Relationship between overweight and obesity with the progression of chronic kidney disease in patients at the Naval Medical Center in Mexico. Enfermería Nefrologica, 26, 60–66.

  33. Meng, X., Wang, X., Yin, S. Li, H. 2023. Few-shot image classification algorithm based on attention mechanism and weight fusion. Journal of Engineering and Applied Science, 70, 14.

  34. Misra, P., Yadav, A.S. 2020. Improving the classification accuracy using recursive feature elimination with cross-validation. International Journal on Emerging Technologies, 11, 659–665.

  35. Nafis, N.S.M., Awang, S. 2021. An enhanced hybrid feature selection technique using term frequency-inverse document frequency and support vector machine-recursive feature elimination for sentiment classification. IEEE Access, 9, 52177–52192.

  36. Nithya, B., Ilango, V. 2019. Evaluation of machine learning based optimized feature selection approaches and classification methods for cervical cancer prediction. Applied Sciences, 1, 1–16.

  37. Payal, M., Kumar, K.S., Kumar, T.A. 2022. Recent advances of Machine Learning Techniques in Biomedicine. International Journal of Multidisciplinary Research in Science, Engineering and Technology, 5, 772–779.

  38. Peter, B., Bruce, A., Gedeck, P. 2020. Practical Statistics for Data Scientists. 2nd Edition. O'Reilly Media, Inc. U.S.A.

  39. Pouragha, H., Amiri, M., Saraei, M., Pouryaghoub, G., Mehrdad, R. 2021. Body impedance analyzer and anthropometric indicators; Predictors of metabolic syndrome. Journal of Diabetes and Metabolic Disorders, 20, 1169–1178.

  40. Pribyl, M.I., Smith, J.D., Grimes, G.R. 2011. Accuracy of the Omron HBF-500 body composition monitor in male and female college students. International Journal of Exercise Science, 4, 93–101.

  41. Ricciardi, R., Talbot, L.A. 2007. Use of bioelectrical impedance analysis in the evaluation, treatment, and prevention of overweight and obesity. Journal of the American Academy of Nurse Practitioners, 19, 235–241.

  42. Rodriguez-Guzman, L., Diaz-Cisneros, F., Rodriguez-Guzman, E. 2006. Overweight and obesity in teachers. Anales de la Facultad de Medicina, 67, 224–229.

  43. Rodrigues-Rodrigues, T., Viera Gomes, A.C, Rodrigues Neto, G. 2018. Nutritional status and eating habits of professors of health area. International Journal of Sport Studies for Health, 1, e64335.

  44. Russo, M.P., Grande-Ratti, M.F., Burgos, M.A., Molaro, A.A., Bonella, M.B. 2023. Prevalence of diabetes, epidemiological characteristics and vascular complications. Archivos de Cardiología de México, 93, 30–36.

  45. Safaei, M., Sundararajan, E.A., Driss, M., Boulila, W., Shapi'i, A. 2021. A systematic literature review on obesity: Understanding the causes & consequences of obesity and reviewing various machine learning approaches used to predict obesity, Computers in Biology and Medicine, 136, 104754.

  46. Sanabria-Arenas, M., Paz-Wilches, J., Laganis-Valcarcel, S., Muñoz-Porras, F., Lopez-Jaramillo, P., Vesga-Guald, J., Perea-Buenaventura, D., Sanchez-Pedraza, R. 2015. Dialysis initiation and mortality in a population with chronic kidney disease in Colombia. Revista de la Facultad de Medicina, 63, 209–216.

  47. Sanchez Soto, J.M., Martinez Reyes, M., Quintero Soto, M.L., Padilla Loredo, S. 2012. Determinación de obesidad a personal de salud de primer nivel de la Jurisdicción de Nezahualcótotl (México) por medio del índice de masa corporal. Medwave, 12, e5464.

  48. Sandeep, S., Gokulakrishnan, K., Velmurugan, K., Deepa, M., Mohan, V. 2010. Visceral & subcutaneous abdominal fat in relation to insulin resistance & metabolic syndrome in non-diabetic south Indians. Indian Journal of Medical Research, 131, 629–635.

  49. Senan, E.M., Al-Adhaileh, M.H., Alsaade, F.W., Aldhyani, T.H.H., Alqarni, A.A., Alsharif, N., Uddin, M.I., Alahmadi, A.H., Jadhav, M.E., Alzahrani, M.Y. 2021. Diagnosis of chronic kidney disease using effective classification algorithms and recursive feature elimination techniques. Journal of Healthcare Engineering, 2021, 1–10.

  50. Karim, S., Qadir, A., Farooq, U., Shakir, M., Laghari, A.A. 2023. Hyperspectral imaging: A review and trends towards medical imaging. Current Medical Imaging, 19, 417–427.

  51. Shamah-Levy, T., Romero-Martínez, M., Barrientos-Gutiérrez, T., Cuevas-Nasu, L., Bautista-Arredondo, S., Colchero, M.A., GaonaPineda, E.B., Lazcano-Ponce, E., Martinez-Barnetche, J., Alpuche-Arana, C., Rivera-Dommarco, J. 2021. Encuesta nacional de salud y nutrición 2020 sobre Covid-19. Resultados nacionales. Cuernavaca, México: Instituto Nacional de Salud Pública, 135–152.

  52. Shamah-Levy, T., Vielma-Orozco, E., Heredia-Hernández, O., Romero-Martínez, M., Mojica-Cuevas, J., Cuevas-Nasu, L., Santaella-Castell, J.A., Rivera-Dommarco, J. 2020. Encuesta nacional de salud y nutrición 2018-19: Resultados nacionales. Cuernavaca, México: Instituto Nacional de Salud Pública, 171–172.

  53. Das, S., Adhikary, A., Laghari, A. A., Mitra, S. 2023. Eldo-care: EEG with kinect sensor based telehealthcare for the disabled and the elderly. Neuroscience Informatics, 100130.

  54. Sparling, P.B. 2007. Obesity on campus. Preventing Chronic Disease, 4, A72.

  55. Šprogar, M., Kokol, P., Zorman, M., Podgorelec, V., Yamamoto, R., Masuda, G., Sakamoto, N. 2001. Supporting medical decisions with vector decision trees. In MEDINFO 2001, 552–556. IOS Press.

  56. Strzelecki, M., Badura, P. 2022. Machine Learning for Biomedical Application. Applied Sciences, 12, 1–5.

  57. Teng, L., Qiao, Y., Shafiq, M., Srivastava, G., Javed, A.R., Gadekallu, T.R, Yin, S. 2023. FLPK-BiSeNet: Federated learning based on priori knowledge and bilateral segmentation network for image edge extraction. IEEE Transactions on Network and Service Management, 20, 1529–1542.

  58. Wilson, S.L., Gallivan, A., Kratzke, C., Amatya, A. 2012. Nutritional status and socio-ecological factors associated with overweight/obesity at a rural-serving US-Mexico border university. Rural and Remote Health, 12, 1–15.


ARTICLE INFORMATION

Received: 2023-07-18
Revised: 2023-08-25
Accepted: 2023-09-18
Available Online: 2023-12-01


Cite this article:

Zaira, P.-R., de los Angeles, R.M.D.L., Ulises, P.-R., Luis, A.O.J., Javier, M.L.F. 2023. Prediction of metabolic ageing in higher education staff using machine learning: A pilot study. International Journal of Applied Science and Engineering, 20, 2023247. https://doi.org/10.6703/IJASE.202312_20(4).009

  Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Other people also read ...

doaj logo

 

2.9
2023CiteScore
 
 
67th percentile
Powered by  Scopus
 
 

SCImago Journal & Country Rank

IJASE - Most Read Articles

IJASE - Most popular articles