Heart Disease Prediction Model

Project Overview

This project is a collaborative effort to develop a machine learning model capable of predicting the likelihood of heart disease based on various health indicators. The project was undertaken as part of a year-long university course at the University of Essex and involved multiple stages of data preprocessing, exploratory data analysis, model building, and evaluation.

Table of Contents

• Project Overview
• Dataset
• Exploratory Data Analysis (EDA)
• Data Preprocessing
• Model Development
• Model Evaluation
• Feature Selection
• Results
• Technologies Used

Dataset

The dataset used in this project is a collection of medical data related to heart disease. It includes the following features:

• Age: The age of the patient.
• Sex: The gender of the patient (0 = female, 1 = male).
• Chest Pain Type: Type of chest pain experienced by the patient.
• Resting Blood Pressure: Resting blood pressure (in mm Hg).
• Cholesterol: Serum cholesterol in mg/dl.
• Fasting Blood Sugar: Fasting blood sugar level (0 = <120 mg/dl, 1 = >120 mg/dl).
• Resting ECG: Resting electrocardiographic results.
• Max Heart Rate: Maximum heart rate achieved.
• Exercise Induced Angina: Exercise-induced angina (1 = yes, 0 = no).
• ST Depression: Depression induced by exercise relative to rest.
• ST Slope: The slope of the peak exercise ST segment.
• Number of Major Vessels: Number of major vessels colored by fluoroscopy.
• Thallium Stress Test: Results of a Thallium stress test.
• Condition: Diagnosis of heart disease (1 = presence, 0 = absence).

Exploratory Data Analysis (EDA)

Exploratory Data Analysis was conducted to understand the distribution of data and the relationships between features. Key visualizations include:

• Condition Ratio: A pie chart showing the distribution of patients with and without heart disease.
• Sex Ratio: A pie chart depicting the gender distribution within the dataset.
• Risk Factors: Bar charts and distribution plots for key risk factors such as fasting blood sugar, resting blood pressure, cholesterol levels, and maximum heart rate.

Data Preprocessing

The data preprocessing steps included:

• Renaming Columns: The original column names were renamed for clarity.
• Missing Values: The dataset was checked for missing values, and appropriate handling methods were applied.
• Encoding Categorical Variables: The target variable (Condition) was label-encoded to facilitate model training.
• Train-Test Split: The dataset was split into training (80%) and testing (20%) sets.
• Normalization: Features were normalized using MinMaxScaler to ensure that all values are within the same range.

Model Development

Several machine learning models were developed and evaluated:

• Logistic Regression: A simple linear model used as a baseline.
• Random Forest Classifier: An ensemble method that uses multiple decision trees to improve prediction accuracy.
• Support Vector Machine (SVM): A model that finds the optimal hyperplane to separate data into different classes.
• XGBoost: A gradient boosting algorithm known for its performance in classification tasks.

Model Evaluation

Model performance was evaluated using several metrics:

• Accuracy: The percentage of correct predictions out of total predictions.
• Confusion Matrix: A matrix showing true positives, true negatives, false positives, and false negatives.
• Classification Report: A detailed report including precision, recall, F1-score, and support for each class.

Feature Selection

Feature selection was performed using:

• RandomForestClassifier: Used to determine feature importance and select the most relevant features.
• SelectFromModel: A method used to reduce the feature set to only the most significant predictors, improving model efficiency and interpretability.

Results

The following table summarizes the accuracy of different models before and after hyperparameter tuning:

Model	Accuracy Before Tuning (%)	Accuracy After Tuning (%)
Logistic Regression	85.25	86.89
Random Forest Classifier	81.97	86.89
Support Vector Machine	85.25	88.52
XGBoost Classifier	86.89	81.97

Technologies Used

• Python: The programming language used for all aspects of the project.
• Pandas: For data manipulation and analysis.
• Scikit-learn: For implementing machine learning algorithms and preprocessing.
• XGBoost: For advanced model building.
• Matplotlib & Seaborn: For data visualization.
• Google Colab: For collaboration and cloud-based execution.