Diabetes Detection Models in Mexican Patients by Combining Machine Learning Algorithms and Feature Selection Techniques for Clinical and Paraclinical Attributes: A Comparative Evaluation

Table 3

Feature description [11].

Feature	Description
Age	Patient age at the time of analysis.
Gender	Patient gender (0—male/1—female).
Education	Studies concluded by the patient (1: elementary school, 2: secondary school, 3: high school, 4: bachelor’s degree).
Weight	Patient weight in kilograms.
Height	Patient height in centimeters.
Waist	Patient waist perimeter in centimeters.
Hip perimeter	Patient hip perimeter in centimeters.
BMI	Body mass index based on weight and height of a patient.
WHR	Waist hip ratio based on the circumference of the waist to that of the hips.
SBP	Systolic blood pressure based on the pressure in the blood vessels when the heart beats.
DBP	Diastolic blood pressure based on the pressure in the blood vessels when the heart rests between beats.
Glucose	Blood glucose levels in terms of milligrams.
MMO glucose	Blood glucose levels in terms of a molar concentration.
Insulin	Patient insulin in the blood.
HOMA	Homeostatic model assessment based on insulin resistance and beta-cell function.
Cholesterol	Fat-like substance that is found in all cells in the patient body.
LDL	Stands of low-density lipoprotein in the patient body.
HDL	Stands for high-density lipoprotein in the patient body.
TR	Triglycerides based on a type of fat (lipids) found in the patient body.