Overview / Usage

Cardiovascular Disease Prediction with OneAPI is a project aimed at developing a machine learning model to predict the risk of cardiovascular disease in individuals. By leveraging advanced algorithms and Intel technologies, this work addresses the critical need for accurate and timely diagnosis of cardiovascular conditions. The research conducted in this project can be applied in real-world healthcare settings to assist healthcare providers in early detection, prevention, and personalized treatment plans for patients. The project's outcomes have the potential to revolutionize cardiovascular care and improve patient outcomes.

Methodology / Approach

The methodology for Cardiovascular Disease Prediction with OneAPI involves several key steps. We start by collecting a comprehensive dataset of patient health records, including medical history, lifestyle factors, and diagnostic tests. We preprocess and clean the data to ensure its quality and reliability. Next, we employ machine learning algorithms, such as logistic regression or decision trees, to train a predictive model using the OneAPI framework. We evaluate the model's performance using metrics like accuracy, precision, and recall. Additionally, we leverage Intel technologies, such as optimized libraries and hardware acceleration, to enhance computational efficiency and speed up the training process. Our development adheres to industry standards and best practices, ensuring robustness and reliability in the model. Overall, our methodology combines data preprocessing, machine learning techniques, and the power of OneAPI and Intel technologies to provide accurate and efficient cardiovascular disease prediction.

Technologies Used

The development of Cardiovascular Disease Prediction with OneAPI involves the following technologies, libraries, tools, software, hardware, and Intel technologies:

1. Technologies: Python, Machine Learning, Data Science, OneAPI
2. Libraries: NumPy, Pandas, Scipy, Sklearn
3. Tools: Jupyter Notebook, Git, GitHub
4. Software: Intel Distribution for Python, Intel oneAPI Toolkits
5. Intel Technologies: Intel oneAPI Base Toolkit, Intel oneAPI Math Kernel Library (MKL), Intel oneDAL, Intel Distribution for Python

These technologies and tools enable us to perform data analysis, implement machine learning algorithms, visualize data, and utilize the optimized capabilities of Intel processors and toolkits to enhance performance and efficiency in cardiovascular disease prediction.

Repository

https://github.com/rithvika7495/Cardiovascular-Disease-Prediction-With-OneAPI