Cardiovascular Disease Prediction

Data

The dataset consists of 70 000 records of patients data: 11 features and a target variable.

Input features could be considered as belonging to the following 3 types:

• Objective: factual information;
• Examination: results of medical examination;
• Subjective: information given by the patient.

Features:

Feature Description	Feature Type	Feature Name	Data Type
Age	Objective Feature	age	int (days)
Height	Objective Feature	height	int (cm)
Weight	Objective Feature	weight	float (kg)
Gender	Objective Feature	gender	Categorical code
Systolic blood pressure	Examination Feature	ap_hi	int
Diastolic blood pressure	Examination Feature	ap_lo	int
Cholesterol	Examination Feature	cholesterol	1: normal, 2: above normal, 3: well above normal
Glucose	Examination Feature	gluc	1: normal, 2: above normal, 3: well above normal
Smoking	Subjective Feature	smoke	binary
Alcohol intake	Subjective Feature	alco	binary
Physical activity	Subjective Feature	active	binary
Presence or absence of cardiovascular disease	Target Variable	cardio	binary

All of the dataset values were collected at the moment of medical examination.

Task

The goal of the project is to analyze Cardiovascular Disease dataset to find which factors are related to the heart diseases.

Used Tools & Libraries

numpy, pandas, matplotlib, seaborn, sklearn, lightgbm, catboost, pytorch