Predicting Heart Disease with Machine Learning: A Python Project Walkthrough

Predicting Heart Disease with Machine Learning: A Python Project Walkthrough

Introduction

As part of my summer training, I developed a machine learning model to predict heart disease. This project helped me understand the practical application of ML algorithms in healthcare and strengthened my Python and data analysis skills.

Problem Statement

Heart disease is one of the leading causes of death worldwide. Using machine learning, we can create early warning systems that help healthcare professionals make better decisions based on data.

Dataset

I used the UCI Heart Disease dataset from Kaggle, which includes features such as:

• Age

• Sex

• Chest Pain Type

• Resting Blood Pressure

• Cholesterol

• Fasting Blood Sugar

• Maximum Heart Rate

• Exercise Induced Angina

• Thalassemia

Technologies Used

• Language: Python

• Libraries: Pandas, NumPy, Scikit-learn, Matplotlib

• ML Algorithms: Logistic Regression, Random Forest, K-Nearest Neighbors (KNN)

Steps Followed

1. Data Preprocessing

• Handled missing values

• Converted categorical values using one-hot encoding

• Normalized numerical features

2. Exploratory Data Analysis

• Visualized distributions and correlations using matplotlib and seaborn

• Found significant features like chest pain type and maximum heart rate

3. Model Building

• Trained Logistic Regression, Random Forest, and KNN classifiers

• Tuned hyperparameters using GridSearchCV

4. Evaluation

• Used Accuracy, Precision, Recall, and Confusion Matrix for performance comparison

Results

• Random Forest gave the best performance with an accuracy of around 89%

• Logistic Regression and KNN followed with solid but slightly lower scores

• Key predictors: Chest Pain Type, Thalassemia, Maximum Heart Rate

Conclusion

This project gave me hands-on experience with building machine learning models, working with healthcare data, and interpreting results. It also showcased how AI can improve medical diagnostics by identifying patterns that might be missed by traditional methods.

GitHub Code: https://github.com/EklavyaGupta1112

Connect with me:

• LinkedIn: https://www.linkedin.com/in/eklavyagupta1112/

• GitHub: https://github.com/EklavyaGupta1112