Heart Disease Prediction

Introduction

Heart disease is a major health issue globally, responsible for numerous deaths each year. Early detection and treatment can significantly improve patient outcomes. This project aims to develop a predictive model for heart disease using patient health metrics, aiding in the timely diagnosis and treatment of this condition.

Dataset

The dataset used for this project contains the following features:

• age: Age of the patient
• sex: Sex of the patient (1 = male; 0 = female)
• cp: Chest pain type (0, 1, 2, 3)
• trestbps: Resting blood pressure (in mm Hg on admission to the hospital)
• chol: Serum cholesterol in mg/dl
• fbs: Fasting blood sugar > 120 mg/dl (1 = true; 0 = false)
• restecg: Resting electrocardiographic results (0, 1, 2)
• thalach: Maximum heart rate achieved
• exang: Exercise induced angina (1 = yes; 0 = no)
• oldpeak: ST depression induced by exercise relative to rest
• slope: The slope of the peak exercise ST segment (0, 1, 2)
• ca: Number of major vessels (0-4) colored by fluoroscopy
• thal: Thalassemia (0 = normal; 1 = fixed defect; 2 = reversible defect)
• target: Presence of heart disease (1 = yes; 0 = no)

Project Workflow

1. Data Collection and Processing:

2. Exploratory Data Analysis (EDA):

   • Summary statistics of the dataset.
   • Distribution plots for each feature, comparing patients with and without heart disease.
   • Correlation matrix to understand relationships between features.

3. Modeling:

   • Splitting the data into training and testing sets.
   • Training machine learning model (logistic regression).
   • Evaluating model performance using appropriate metrics (accuracy).

4. Conclusion:

   • Make Predictions.
   • Summarizing findings.