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Table 2 Model building strategies

From: Optimizing cardiovascular disease mortality prediction: a super learner approach in the tehran lipid and glucose study

Model

The feature considered

Model 1:

All variables in their original scales besides the history of drugs

Age, Sex, Smoking status, Education, Marital Status, Family History of Stroke, SBP, DBP, BMI, Waist, Hip, FBS, TG, HDL, Physical Activity, Lipid Drug, Anti-Hypertension Drug, Aspirin, Corticosteroid.

Model 2:

Transformed variables; the effect of changing the continuous to the discrete state of the features.

Age, Sex, Smoking status, Education, Marital Status, Family History of Stroke, Anti-Hypertension drug, BMI categories, Waist-to-Height Ratio, T2DM, high TG, low HDL, Physical Activity

Model 3:

Transformed variables; the effect of changing the continuous to the discrete state of the features.

Age, Sex, Smoking status, Education, Marital Status, Family History of Stroke, Anti-Hypertension Drug, BMI categories, Waist-to-Hip Ratio, T2DM, high TG, low HDL, and Physical Activity

Model 4:

Cardio-metabolic risk factors model; reducing the number of features.

Age, Sex, Smoking status, Education, Marital Status, Family History of Stroke, Cardio-metabolic risk factors*

  1. *Cardio-metabolic risk factors refer to risk factors that increase the chance of experiencing cardiovascular events, such as age, sex, obesity, hypertension, dyslipidemia (high LDL cholesterol, high triglycerides, and low HDL cholesterol), dysglycemia, smoking, abdominal obesity, lack of consumption of fruits and vegetables, and sedentary lifestyle. Abbreviation: SBP: systolic blood pressure; DBP: diastolic blood pressure; BMI: body mass index; FBS: fasting blood sugar; TG: total triglyceride; HDL: high-density lipoprotein; T2DM: type 2 diabetes mellitus
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BMC Medical Informatics and Decision Making

ISSN: 1472-6947

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