Heart Disease Predictor using Machine Learning

Website link: https://heart-disease-predictor-system-tarun.streamlit.app/

The main objective of the project is to develop a highly accurate Heart Disease predictor system using Machine Learning Algorithms.

Programmming Language:

1. Python

Software:

1. Jupyter Notebook
2. PyCharm
3. Git
4. GitHub
5. Streamlit

Tools and packages:

1. Data manipulation and handling: Pandas, NumPy
2. Data Visualizations: Seaborn and Matplotlib
3. ML Modelling: Scikit-learn
4. Model Evaluation: Scikit-learn
5. Python app: Streamlit

Models with accuracy:

1. Logistic Regression: with an accuracy of 82.14%
2. XGBoost Classifier: with an accuracy of 99.03%
3. MLP CClassifier: with an accuracy of 85.06%

Application demo:

Application Areas:

1. Hospitals and Clinics

a. Early detection and diagnosis of heart disease in patients. b. Assisting cardiologists and healthcare providers in decision-making. c. Risk assessment for patients with underlying health conditions.

2. Telemedicine

a. Remote monitoring of patient health. b. Providing predictive analysis during virtual consultations.

3. Health Insurance

a. Risk assessment for policy underwriting. b. Preventive healthcare incentives based on predictive insights. c. Fitness and Wellness Programs

Usecases:

1. Preventive Care

a. Identifying individuals at high risk of heart disease and recommending lifestyle or medical interventions. b. Screening asymptomatic patients during routine check-ups.

2. Clinical Decision Support

a. Providing doctors with predictive insights to prioritize patient care. b. Guiding treatment plans based on disease likelihood.