Cardiovascular Disease
Prediction

Project Overview

The Cardiovascular Disease Prediction project aims to predict the occurrence of cardiovascular disease in patients based on their medical records and history. By analyzing various factors, the project calculates the probability of cardiovascular disease developing in a patient. This predictive model is a valuable tool in identifying individuals who are at higher risk and enabling early intervention.

Data Dictionary

Feature Description
General Health general health condition
Checkup Last checkup
Excersise Does the patient excersise
Heart Disease Does the patient have heart disease
Skin Cancer Does the patient have skin cancer
Other Cancer Does the patient have other cancer
Depression Does the patient have depression
Diabetes Does the patient have diabetes
Arthritis Does the patient have arthritis
Sex patient's gender
Age-Category patient's age category
BMI patient's BMI
Smoking History patient's smoking history
Alcohol Consumption patient's alcohol consumption
Fruit Consumption patient's fruit consumption
Green Vegetable Consumption patient's green vegetable consumption
Fried Potato Consumption patient's fried potato consumption

Impact

Through thorough exploratory data analysis, significant insights were revealed about the factors contributing to cardiovascular disease:

  • Age plays a crucial role, with individuals above the age of 55 being more prone to cardiovascular disease. The risk escalates with age, reaching a peak in patients aged 80 and above.
  • Higher BMI is correlated with an increased likelihood of cardiovascular disease.
  • Surprisingly, exercise can have varying effects based on age. While exercise generally reduces risk, older patients who exercise extensively might face elevated risks due to increased heart strain.
  • Dietary habits significantly influence the disease. Patients consuming more fruits and green vegetables exhibit lower susceptibility to cardiovascular disease, while those consuming fried potatoes are more prone.
  • Smoking history is a notable risk factor.
  • Intriguingly, medical history concerning cancer, arthritis, diabetes, and depression showed no significant impact on cardiovascular disease risk.

    • This project's predictions and insights empower healthcare professionals to identify high-risk patients and implement preventive measures, ultimately reducing the prevalence of cardiovascular disease.

    In conclusion, the Cardiovascular Disease Prediction project underscores the importance of data-driven approaches in healthcare, aiding in the proactive management of patients' cardiovascular health.

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