Heart Failure Detection Using Quantum-Enhanced Machine Learning and Traditional Machine Learning Techniques for Internet of Artificially Intelligent Medical Things
Table 3
Description of dataset attributes.
Attribute
Description
Input
Age
Age of the patients
Years
Sex
Sex (;)
Float
Trestbps
Resting blood pressure (in mmHg on admission to the hospital)
mm/Hg
CP
Chest pain type-- value 1: typical angina -- value 2: atypical angina -- value 3: nonanginal pain -- value 4: asymptomatic
Float
Cholesterol
Serum cholesterol
mg/dl
Fps
() (;)
Float
Restecg
Resting electrocardiographic results-- value 0: normal -- value 1: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV) -- value 2: showing probable or definite left ventricular hypertrophy by Estes’ criteria
Float
Thalach
Maximum heart rate achieved
Binary
Exang
Exercise induced angina (;)
Int
Old peak
ST depression induced by exercise relative to rest
Continuous
Slope
The slope of the peak exercise ST segment-- value 1: upsloping -- value 2: flat -- value 3: downsloping number of major vessels (0-3) colored by flourosopy
Float
CA
Follow up period number of major vessels (0-3) colored by flourosopy
Float
Thal
;;
Float
Target
Whether person suffering through heart disease or not
