Challenge

This project aims to predict the likeliness of a patient having a heart attack based on basic information such as age, sex, blood pressure, cholesterol levels, etc.

Dataset The dataset used in this project is obtained from Kaggle. It contains various features including:

Age

• Sex
• Chest pain type (cp)
• Resting blood pressure (trtbps)
• Serum cholesterol (chol)
• Fasting blood sugar (fbs)
• Resting electrocardiographic results (restecg)
• Maximum heart rate achieved (thalachh)
• Exercise induced angina (exng)
• ST depression induced by exercise relative to rest (oldpeak)
• Slope of the peak exercise ST segment (slp)
• Number of major vessels colored by fluoroscopy (caa)
• Thalassemia (thall)

Model Architecture The model architecture is a simple neural network implemented using TensorFlow. It consists of:

Input layer

• Dense hidden layer with ReLU activation
• Dropout layer
• Output layer with sigmoid activation

Solution

Technologies

Git

Github