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PISIoT: A Machine Learning and IoT-Based Smart Health Platform for Overweight and Obesity Control
| dc.contributor.author | Machorro-Cano, Isaac | |
| dc.contributor.author | Alor-Hernández, Giner | |
| dc.contributor.author | Paredes Valverde, Mario Andres | |
| dc.contributor.author | Ramos-Deonati, Uriel | |
| dc.contributor.author | Sánchez-Cervantes, José Luis | |
| dc.contributor.author | Rodríguez-Mazahua, Lisbeth | |
| dc.date.accessioned | 2021-07-07T15:43:57Z | |
| dc.date.available | 2021-07-07T15:43:57Z | |
| dc.date.issued | 2019-07-28 | |
| dc.identifier.citation | Machorro-Cano, Isaac, Giner Alor-Hernández, Mario A. Paredes-Valverde, Uriel Ramos-Deonati, José L. Sánchez-Cervantes, and Lisbeth Rodríguez-Mazahua. 2019. "PISIoT: A Machine Learning and IoT-Based Smart Health Platform for Overweight and Obesity Control" Applied Sciences 9, no. 15: 3037. https://doi.org/10.3390/app9153037 | es |
| dc.identifier.issn | 2076-3417 | |
| dc.identifier.other | https://doi.org/10.3390/app9153037 | |
| dc.identifier.uri | http://repositorios.orizaba.tecnm.mx:8080/xmlui/handle/123456789/494 | |
| dc.description | Overweight and obesity are affecting productivity and quality of life worldwide. The Internet of Things (IoT) makes it possible to interconnect, detect, identify, and process data between objects or services to fulfill a common objective. The main advantages of IoT in healthcare are the monitoring, analysis, diagnosis, and control of conditions such as overweight and obesity and the generation of recommendations to prevent them. However, the objects used in the IoT have limited resources, so it has become necessary to consider other alternatives to analyze the data generated from monitoring, analysis, diagnosis, control, and the generation of recommendations, such as machine learning. This work presents PISIoT: a machine learning and IoT-based smart health platform for the prevention, detection, treatment, and control of overweight and obesity, and other associated conditions or health problems. Weka API and the J48 machine learning algorithm were used to identify critical variables and classify patients, while Apache Mahout and RuleML were used to generate medical recommendations. Finally, to validate the PISIoT platform, we present a case study on the prevention of myocardial infarction in elderly patients with obesity by monitoring biomedical variables. | es |
| dc.description.abstract | Overweight and obesity are affecting productivity and quality of life worldwide. The Internet of Things (IoT) makes it possible to interconnect, detect, identify, and process data between objects or services to fulfill a common objective. The main advantages of IoT in healthcare are the monitoring, analysis, diagnosis, and control of conditions such as overweight and obesity and the generation of recommendations to prevent them. However, the objects used in the IoT have limited resources, so it has become necessary to consider other alternatives to analyze the data generated from monitoring, analysis, diagnosis, control, and the generation of recommendations, such as machine learning. This work presents PISIoT: a machine learning and IoT-based smart health platform for the prevention, detection, treatment, and control of overweight and obesity, and other associated conditions or health problems. Weka API and the J48 machine learning algorithm were used to identify critical variables and classify patients, while Apache Mahout and RuleML were used to generate medical recommendations. Finally, to validate the PISIoT platform, we present a case study on the prevention of myocardial infarction in elderly patients with obesity by monitoring biomedical variables. | es |
| dc.description.sponsorship | Funding This research was funded by the National Technological Institute of Mexico (TecNM), grant number 6544.18-P—Support program for scientific and technological research 2018. Acknowledgments This work was supported by the National Technological Institute of Mexico (TecNM) and sponsored by Mexico’s National Council of Science and Technology (CONACYT) and the Secretariat of Public Education (SEP) through the PRODEP project (Programa para el Desarrollo Profesional Docente). The authors wish to acknowledge the contribution of the health personnel and research professors of the Universidad del Papaloapan (UNPA) (Doctor Jolbert Jair Matus Manuel, nurse and M.S.P. Lina María Reyes Pérez, nutritionist and M.S.A.N. Sulik Saraí Luna Gómez Lechuga, M.C. Mónica Guadalupe Segura Ozuna, and Doctor and M.M.F Arnulfo Cárdenas Reyes). | es |
| dc.language.iso | en_US | es |
| dc.publisher | MDPI Publishing | es |
| dc.relation.ispartofseries | Applied Sciences; | |
| dc.subject | biomedical variables | es |
| dc.subject | Internet of Things | es |
| dc.subject | machine learning | es |
| dc.subject | overweight and obesity | es |
| dc.title | PISIoT: A Machine Learning and IoT-Based Smart Health Platform for Overweight and Obesity Control | es |
| dc.type | Article | es |
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