Warm Up: Machine Learning with a Heart Hosted By DrivenData

2 months left
Glory!

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)

Feature data example


Here's an example of one of the rows in the dataset so that you can see the kinds of values you might expect in the dataset. Some are binary, some are integers, some are floats, and some are categorical. There are no missing values.

field value
slope_of_peak_exercise_st_segment 2
thal normal
resting_blood_pressure 125
chest_pain_type 3
num_major_vessels 0
fasting_blood_sugar_gt_120_mg_per_dl 1
resting_ekg_results 2
serum_cholesterol_mg_per_dl 245
oldpeak_eq_st_depression 2.4
sex 1
age 51
max_heart_rate_achieved 166
exercise_induced_angina 0

Performance metric


Performance is evaluated according to binary log loss.

Submission format


The format for the submission file is two columns with the patient_id and heart_disease_present. This competition uses log loss as its evaluation metric, so the heart_disease_present values you should submit are the probabilities that a patient has heart disease (not the binary label).

For example, if you predicted...

patient_id heart_disease_present
olalu7 0.5
z9n6mx 0.5
5k4413 0.5
mrg7q5 0.5
uki4do 0.5
... ...
bwoyg6 0.5
j8i7ve 0.5
t2zn1n 0.5
oxf8kj 0.5
aeiv0y 0.5

Your .csv file that you submit would look like:

patient_id,heart_disease_present
olalu7,0.5
z9n6mx,0.5
5k4413,0.5
mrg7q5,0.5
uki4do,0.5
kev1sk,0.5
9n6let,0.5
jxmtyg,0.5
51s2ff,0.5
...
5bbknr,0.5
hr6pjx,0.5
r4hsar,0.5
4cezdf,0.5
palhcc,0.5
bwoyg6,0.5
j8i7ve,0.5
t2zn1n,0.5
oxf8kj,0.5
aeiv0y,0.5

Good luck!


Good luck and enjoy this problem! If you have any questions you can always visit the user forum!