Heart-Attack-Analysis-Prediction

This project focuses on building a Machine Learning (ML) model to predict how prone a person is to a heart attack based on various health and lifestyle indicators.
The project was developed as part of the course Introduction to Machine Learning (CSL2010). The final implementation can be viewed in heart_attack_analysis_and_prediction.ipynb file within the repository.

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📊 Dataset Overview

The dataset contains 303 samples and 14 columns, including patient details and medical observations.

Features:

• Age: Patient's age in years.
• Sex: Patient's gender.
• cp: Type of chest pain experienced:
  • 1: Typical angina
  • 2: Atypical angina
  • 3: Non-anginal pain
  • 4: Asymptomatic
• trtbps: Resting blood pressure (mm Hg).
• chol: Cholesterol level (mg/dl).
• fbs: Fasting blood sugar > 120 mg/dl (1 = true, 0 = false).
• rest_ecg: Resting electrocardiogram results:
  • 0: Normal
  • 1: ST-T wave abnormality
  • 2: Left ventricular hypertrophy (Estes' criteria).
• thalach: Maximum heart rate achieved during physical exertion.
• exng: Exercise-induced angina (1 = yes, 0 = no).
• oldpeak: Previous peak (ST depression induced by exercise).
• slp: Slope of the peak exercise ST segment.
• caa: Number of major vessels visible through fluoroscopy (range: 0-3).
• thall: Thal Rate.

Output Variable:

• target: Predicted likelihood of a heart attack (0 = lower risk, 1 = higher risk).

Column Categorization:

• categorical_cols = ["sex", "cp", "fbs", "exng", "restecg", "thall", "caa", "slp"]
• continuous_cols = ["age", "trtbps", "chol", "thalachh", "oldpeak"]

🛠️ Data Preprocessing and Analysis Workflow

Exploratory Data Analysis (EDA):

Visualizations like histograms, box plots, and scatter plots are created to identify feature distributions, relationships, and potential outliers.

Train-Test Split:

The dataset is split into training and testing sets to evaluate model performance while avoiding data leakage.

Scaling Continuous Features:

Continuous features are standardized using StandardScaler to ensure they have a mean of 0 and a standard deviation of 1.

🚀 Models Implemented

The following ML models were implemented and evaluated for this project:

• Logistic Regression
• Gaussian Naive Bayes
• Bernoulli Naive Bayes
• Support Vector Machine (SVM)
• Decision Tree Classifier
• K-Nearest Neighbors (KNN)
• Multi-Layer Perceptron (MLP)

📈 Results

Model Performance:

Each model was evaluated using metrics such as accuracy, precision, recall, and F1-score. Comparative analysis of these metrics helped identify the most effective model for predicting heart attack risks.

🧑‍💻 Team Members

• Ayaan Choudhury (B23ME1013)
• Arush Aaron John (B23CH1009)
• Harsh Nandan Shukla (B23MT1019)
• Saketh Babburu (B23ME1059)

🤝 Acknowledgments

This project was completed under the guidance of Prof. Avinash Sharma for the course Introduction to Machine Learning (CSL2010).