Dataset taken from Driven Data
Problem description Your goal is to predict the binary class heart_disease_present, which represents whether or not a patient has heart disease:
0 represents no heart disease present 1 represents heart disease present
Dataset There are 14 columns in the dataset, where the patient_id column is a unique and random identifier. The remaining 13 features are described in the section below.
- slope_of_peak_exercise_st_segment (type: int): the slope of the peak exercise ST segment, an electrocardiography read out indicating quality of blood flow to the heart
- thal (type: categorical): results of thallium stress test measuring blood flow to the heart, with possible values normal, fixed_defect, reversible_defect
- resting_blood_pressure (type: int): resting blood pressure
- chest_pain_type (type: int): chest pain type (4 values)
- num_major_vessels (type: int): number of major vessels (0-3) colored by flourosopy
- fasting_blood_sugar_gt_120_mg_per_dl (type: binary): fasting blood sugar > 120 mg/dl
- resting_ekg_results (type: int): resting electrocardiographic results (values 0,1,2)
- serum_cholesterol_mg_per_dl (type: int): serum cholestoral in mg/dl
- oldpeak_eq_st_depression (type: float): oldpeak = ST depression induced by exercise relative to rest, a measure of abnormality in electrocardiograms
- sex (type: binary): 0: female, 1: male
- age (type: int): age in years
- max_heart_rate_achieved (type: int): maximum heart rate achieved (beats per minute)
- exercise_induced_angina (type: binary): exercise-induced chest pain (0: False, 1: True)