Machine Learning with a Heart

This is my submission for the DrivenData competition Machine Learning with a Heart.

Status

I have tried two simple solutions (one using Random Forest and another using Logistic Regression) obtaining a score of ~0.36258 (Log Loss). I want to move on to something a little more exciting!

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Problem Description

We must predict the binary class heart_disease_present where:

• 0 represents no heart disease present
• 1 represents heart disease present

Dataset

There are 14 columns. patient_id is the unique and random identifier for a patient. The rest are:

• 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 Examples

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

Performance is evaluated using the binary log loss.

Submission Format

Two columns are required: patient_id and heart_disease_present. Because this competition uses a log loss evaluation metric, heart_disease_present should contain the probabilities that patients have heart disease and not the binary label. For example:

patient_id	heart_disease_present
olalu7	0.5
z9n6mx	0.5
...	...