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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

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Predicting heart diseases using Cardiovascular Diseases dataset

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