# PISIoT: A Machine Learning and IoT-Based Smart Health Platform for Overweight and Obesity Control
## 5 Conclusions  
The human body is constantly providing information about one's state of health. This information is obtained through systems or devices that measure, capture or detect values and variables at specific points of the body in an invasive or non-invasive manner. Healthcare personnel use the values of biomedical variables to make decisions on diagnoses and treatments in order to improve patients' health. Across the world, quality of life, particularly among the elderly, is being affected considerably by overweight and obesity. The 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 is necessary to consider other alternatives for data analysis, such as machine learning. Machine learning is a subset of artificial intelligence that consists in studying the algorithms and statistical models used in computer systems in order to achieve specific objectives effectively, based on patterns and inferences.  
At present, there are several challenges in the health sector that provide areas of opportunity for the IoT and machine learning to provide solutions or alternatives that contribute to improving healthcare and quality of life. In this work, we have presented PISIoT: a machine learning and IoT-based smart health platform for the prevention, detection, treatment, and monitoring of overweight and obesity. Weka API and the J48 machine learning algorithm were used to identify critical variables, while Apache Mahout and RuleML were used to generate medical recommendations. Finally, to validate the PISIoT platform, we presented a case study on the prevention of myocardial infarction in elderly patients with obesity by monitoring biomedical variables. The main limitations of PISIoT are that, at the moment, it focuses only on the detection, prevention, treatment, and monitoring of overweight and obesity. Likewise, it uses a type of wearable device and smart scale and only uses the J48 machine learning algorithm.  
In the future, we intend to consider monitoring other chronic degenerative diseases and conditions associated with overweight and obesity, such as high blood pressure, diabetes, cardiovascular diseases, and cancer (of the endometrium, breasts, ovaries, prostate, liver, gallbladder, kidneys, and colon). Likewise, we intend to use other wearable and smart devices to collect various biomedical variables related to chronic degenerative diseases. In addition, we hope to use other machine learning algorithms in order to evaluate and identify those that perform best.  
Conceptualization, G.A.-H. and I.M.-C.; methodology, M.A.P.-V. and I.M.-C.; software, L.R.-M. and U.R.-D.; validation, I.M.-C., U.R.-D., L.R.-M., and G.A.-H.; formal analysis, L.R.-M.; investigation, J.L.S.-C.; resources, G.A.-H.; data curation, M.A.P.-V.; writing--original draft preparation, I.M.-C.; writing--review and editing, G.A.-H. and M.A.P.-V.; visualization, J.L.S.-C.; supervision, L.R.-M.; project administration, G.A.-H.; funding acquisition, G.A.-H. and J.L.S.-C.  
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.  
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 authorswish to acknowledge the contribution of the health personnel and research professors of the Universidad del Papaloapan (UNPA) (Doctor [PERSON], nurse and M.S.P. [PERSON], nutritionist and M.S.A.N. [PERSON], [PERSON], and Doctor and M.M.F [PERSON]).