Project Title:Predicting Heart Disease Using Machine Learning

Objective:

To develop a machine learning model that predicts whether a person is at risk of heart disease based on health-related parameters.

Project Summary:

Heart disease is a leading cause of death globally, and early prediction can save lives. This project uses machine learning techniques to analyze medical data and predict the likelihood of heart disease in a patient. The dataset typically includes attributes such as age, sex, blood pressure, cholesterol levels, chest pain type, resting ECG results, maximum heart rate, and more. The model learns patterns in this data to classify whether a person is likely to have heart disease or not.

Key Components:

Dataset: Heart disease dataset (e.g., Cleveland dataset from UCI repository)

Features: Age, sex, chest pain type, resting BP, cholesterol, fasting blood sugar, ECG results, etc.

Algorithms: Logistic Regression, Random Forest, Decision Tree, k-NN, Support Vector Machine (SVM), XGBoost

Tools & Libraries: Python, Scikit-learn, Pandas, NumPy, Matplotlib, Seaborn

Technologies: Machine Learning, Classification, Health Informatics

Features:

Data cleaning and preprocessing

Feature selection to improve model accuracy

Training and testing different ML models

Performance evaluation using metrics like accuracy, precision, recall, F1-score, and ROC curve

Visual insights into risk factors using plots and heatmaps

Applications:

Early diagnosis tools in healthcare systems

Health monitoring apps and smart devices

Risk assessment for insurance and medical research

Clinical decision support for doctors

Outcome:

This project helps students understand how machine learning can be applied to healthcare problems. It strengthens skills in data handling, model building, and interpretation—while also creating something meaningful that could potentially assist in saving lives.