Cardiovascular diseases (CVDs) are a significant global health concern, contributing to a substantial number of deaths each year. Early detection and prediction of CVDs can greatly aid in preventive healthcare and timely intervention. This project aims to develop a predictive model for cardiovascular diseases using machine learning techniques.

Objective: The primary objective of this project is to predict the likelihood of an individual having cardiovascular disease based on various risk factors and clinical parameters. By leveraging machine learning algorithms, particularly Random Forest Classifier, the model will analyze input data to provide predictions.

Figure 1: It is the main screen of Cardiovascular Disease Predictor.

Figure 2 : Press “Predict” button to see the model accuracy and the likeliness of a person having a cardiovascular disease.

Figure 3: After clicking “OK” button in the message box it shows the message with Black if the person is unlikely to have a cardiovascular disease.

Figure 4: After clicking “OK” button in the message box it shows the message with red if the person is likely to have a cardiovascular disease.

Key Features of the Project:

1. User-Friendly Interface: The project provides a user-friendly graphical interface where users can input relevant information such as age, gender, blood pressure, cholesterol levels, etc.
2. Predictive Model: Utilizes a Random Forest Classifier model trained on a cardiovascular disease dataset to predict the likelihood of an individual having CVDs.
3. Real-Time Prediction: Enables real-time prediction based on user input, providing immediate feedback on the individual's risk of cardiovascular disease.
4. Accuracy Assessment: The project includes an accuracy assessment of the predictive model, giving users insights into the reliability of the predictions.

Technologies and Tools used:

• Tkinter

• Python

• Jupyter Notebook

• Machine Learning

• Data Handling