Analysis of factors impacting Sarcopenia in geriatric patients through the use of data sciences: A Case Study in Tijuana, Mexico

Abstract

Sarcopenia is the loss of muscle mass associated with the ageing process. Moreover, it is a progressive disease affecting older people. In 2017, about 12 million mexican elder people suffered from Sarcopenia; nevertheless, many of them are not aware of their condition. A study conducted by the Instituto Mexicano del Seguro Social (IMSS) estimates that 72.10% of people with Sarcopenia were women, while the rest were men [1]. This study analyses a database which includes the information of 166 geriatric patients from Tijuana, Baja California state. The database encompasses 90 variables, including biomedical information and some demographic information such as age, gender, address, schooling, marital status, level of education, income, profession, and financial support. An analysis to find the weight factors that impact the development of sarcopenia was carried out by generating a decision tree using the database provided by the General Hospital of Tijuana and the support of Orange software. Based on the creation of this tree, the relation and impact of the most important factors was analyzed. Among the three most important risk factors for this disease, besides senescence, the results from the analysis showed that Major Neurocognitive Disorder (MND), Systolic Arterial Hypertension (SAH), and malnutrition are the most important conditions to consider. These obtained results were compared with results retrieved from a study where the analysis was done through a Python simulation using machine learning methods with the same database.