# Machine and Deep Learning Applied to Predict Metabolic Syndrome without a Blood Screening
## 1 Introduction  
Nowadays, chronic degenerative diseases, such as ischemic heart disease, type 2 diabetes mellitus, and cerebrovascular stroke, are the leading causes of morbidity and mortality worldwide; these diseases share one or more metabolic components (glucose intolerance, insulin resistance, central obesity, dyslipidemia, and hypertension) that might co-exist in one individual. The term Metabolic Syndrome (MetS) was coined to express this constellation of metabolic abnormalities [1]. The prevalence of MetS in Mexico is 41% [2] higher than in developed countries, like the United States (34.2%) [3], due to the epidemic proportion that overweight and obesity have taken in our country, affecting not only the adult population, but also young individuals and even children, with obesity being a central, key component of MetS. The evaluation criteria for MetS have been proposed by the National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) [4], the International DiabetesFederation (IDF) [5; 6], and the World Health Organization (WHO) [7]. In order to establish a clinical diagnose of MetS, at least three metabolic abnormalities mostly co-occur in the same individual: elevated blood sugar levels, insulin resistance, abdominal obesity, high blood pressure, and abnormal lipid profile (high blood levels of triglyceride and low blood levels of high-density lipoprotein cholesterol). These evaluation criteria have been useful from a clinical point of view. However, from the general population perspective, its utility is limited, since laboratory screening is needed [8], which can be costly or cumbersome to implement for certain population levels. The above and the fact that most people are unaware of their health condition, and approach health services until the sickness has caused health limitations [9; 10; 11]. For this reason, it is essential to generate tools that can help people to identify pre-clinical conditions, so that preventive measures can be taken at a population level. The application of machine learning and deep learning (DL) algorithms has facilitated the construction of effective models to predict disease diagnoses and the corresponding treatments [12; 13]. However, the appropriate selection of variables (potential risk factors) is decisive in improving the models [14; 15; 16]. In the case of MetS, the use of machine learning and deep learning algorithms, as well as feature selection methods, have achieved significant results in the development of models to support the prediction of this syndrome [17; 18]. Related studies that have omitted blood screening for the diagnosis of the MetS have had promising results. An investigation conducted by [PERSON] et al. [19], proposed a data mining methodology to diagnose MetS without blood screening by applying Artificial Neural Networks (ANN). In that work, hip circumference, dichotomous waist circumference, dichotomous blood pressure, and sex were included; the specificity reported was 82.59%, a Positive Predictive Value (PPV) of 90.54%, and an Area under the Receiver Operating Characteristic (ROC) Curve of 87.36%. [PERSON] et al. [20] constructed a model applying ANN and considering variables, such as Body Mass Index (BMI), Waist Circumference (WC), weight, height, and sex. The authors used PPV as an evaluation metric, obtaining a value of 38.8%. [PERSON] et al. [21] presented a model using ANN for the prediction of MetS, excluding blood screening. The features that result in this study were: gender, age, BMI, Waist-to-height Ratio (WhtR), systolic, and diastolic blood pressures, with a PPV of 85.79% and Negative Predictive Value (NPV) of 83.19%. Another study relates the dietary habits with MetS in the Swedish region. The authors used chi-square analysis [22]. They found four patterns that were defined by clusters that demonstrated a strong association between food and components of MetS.