Research Article

Efficient Model for Coronary Artery Disease Diagnosis: A Comparative Study of Several Machine Learning Algorithms

Table 2

Specifications of the dataset features used in the study.

Feature nameType of featureMissing

AgeNumeric0M = 59 ± 10
WeightNumeric073.83 ± 12
LengthNumeric0165 ± 9.33
SexNominal0Male = 176, female = 127
BMI (body mass index)Numeric027.25 ± 4.01
Diabetes mellitus (DM)Nominal0Yes, no
Hypertension (HTN)Nominal0Yes, no
Current smokerNominal0Yes, no
ExsmokerNominal0Yes, no
Family history (FH)Nominal0Yes, no
ObesityNominal0Yes = 211, no = 92
Chronic renal failure (CRF)Nominal0Yes = 6, no = 297
Cardiovascular diseases (CVA)Nominal0Yes = 5, no = 298
Airway diseasesNominal0Yes = 11, no = 292
Thyroid diseasesNominal0Yes = 7, no = 296
Congestive heart failure (CHF)Nominal0Yes = 1, no = 302
Dyslipidemia (DLP)Nominal0Yes = 112, no = 191
EdemaNominal0Yes, no
Weak peripheral pulseNominal0Yes = 5, no = 298
Systolic murmurNominal0Yes = 41, no = 262
Diastolic murmurNominal0Yes = 9, no = 294
Typical chest painNominal0Yes, no
DyspneaNominal0Yes = 134, no = 169
AtypicalNominal0Yes = 93, no = 210
NonanginalNominal0Yes = 16, no = 287
Catheterization (cath)Nominal0CAD = 216, normal = 87