The goal of this data exploration and predictive analysis is to better understand which health factors affect a patient’s risk for heart disease. To accomplish this, an introduction to the data will be made, along with a graphical analysis of the health factors in the dataset. The predictive modeling process will introduced, giving the background for the evaluation of the logistic regression predictive model. This evaluation will comprise of reviewing performance metrics from the confusion matrix. Finally, an explanation of the model’s calculation will be given for a specific example to show what factors went in to the prediction.
The Heart Disease Dataset selected for this project came from the UCI Machine Learning Repository https://archive.ics.uci.edu/ml/datasets/heart+disease.
Link to my Towards Data Science publication for this can be found at the following: https://towardsdatascience.com/data-science-for-the-heart-c654135ceee5.