This project aims to predict heart disease risk by using Python-based machine learning algorithms, including logistic regression and K-nearest neighbors. Health data is captured in real-time with Arduino-connected heart rate sensors, processed in Jupyter Notebook. By integrating these technologies, the system supports early identification of heart disease, providing healthcare providers with data-driven insights for proactive patient care.
- Programming Language: Python
- Tools: Arduino, Heart Rate Sensor, Pytest, Jupyter Notebook
- Arduino Programming Language: C++ is used for programming the Arduino.
- ML Algorithms: Linear Regression, K-Nearest Neighbors
- ML Techniques and Approaches: Implemented various machine learning techniques and approaches.
- Achieved an accuracy of 87%.
- Testing: Comprehensive testing was performed for this project.
I have attached the complete project report in a folder named Project_PDF, which includes a PDF with information about the project's functionality, output, and the contributions of all team members. Additionally, a PowerPoint presentation (PPT) summarizing the project has also been included in the folder. Please refer to Project_PDF for any further information.