This project used Google Collab as the development environment. Google Collab is a cloud-based Python platform providing access to GPUs for accelerated computation. Python (version 3.8) was used in the Google Collab environment, with additional libraries and frameworks included, such as Scikit-learn, XGBoost, Pandas, NumPy, Matplotlib, and Seaborn, as detailed in the References section. The dataset sources used were gathered from the the UCI Machine Learning Repository from the “Heart Disease” database. Two datasets from this database were used; Cleveland and VA Long Beach datasets. Data cleaning and preprocessing were conducted within Google Colab Notebooks using Python-based libraries, with datasets and code files stored in CSV, Python (.py), and Jupyter Notebook (.ipynb) formats.

Version control was maintained through a GitHub repository that hosted the project’s source code, processed datasets, and supplementary materials. The repository, accessible at [https://github.com/Jdasanja/masters_thesis_final], was updated regularly with a detailed commit history to ensure reproducibility. External tools included the ASCVD Risk Calculator, implemented via an open-source Python package available at [https://github.com/brandones/ascvd/tree/master].