Created a risk prediction model to predict the likelihood of patients developing cardiovascular disease based on bioindicators. Created a secondary application that takes input weights from the logistic regression applied to previous patient data to advise new patients on best practices and lifestyle habits.
For this project I utilized a kaggle dataset: https://www.kaggle.com/datasets/data855/heart-disease
This dataset contains a variety of bioindicators that serve as both direct and indirect indicators to show cardiovascular health through vitals like blood pressure and blood vessel health.
This database contains 76 attributes, but all published experiments refer to using a subset of 14 of them. In particular, the Cleveland database is the only one that has been used by ML researchers to this date. The "goal" field refers to the presence of heart disease in the patient. It is integer-valued from 0 (no presence) to 4.
Acknowledgements Creators:
Hungarian Institute of Cardiology. Budapest: Andras Janosi, M.D. University Hospital, Zurich, Switzerland: William Steinbrunn, M.D. University Hospital, Basel, Switzerland: Matthias Pfisterer, M.D. V.A. Medical Center, Long Beach and Cleveland Clinic Foundation: Robert Detrano, M.D., PhD.