Binary Classification Model for Heart Disease Study Using Python Take 1

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: The purpose of this project is to construct a prediction model using various machine learning algorithms and to document the end-to-end steps using a template. The Heart Disease dataset is a binary classification situation where we are trying to predict one of the two possible outcomes.

INTRODUCTION: The original 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 machine learning researchers to this date. The “num” field refers to the presence of heart disease in the patient. It is integer valued from 0 (no presence) to 4. Experiments with the Cleveland database have concentrated on simply attempting to distinguish presence (values 1,2,3,4) from absence (value 0).

ANALYSIS: The baseline performance of the machine learning algorithms achieved an average accuracy of 74.69%. Two algorithms (Logistic Regression and Stochastic Gradient Boosting) achieved the top accuracy metrics after the first round of modeling. After a series of tuning trials, Logistic Regression turned in the top overall result and achieved an accuracy metric of 82.07%. By using the optimized parameters, the Logistic Regression algorithm processed the testing dataset with an accuracy of 90.10%, which was even better than the prediction accuracy gained from the training data.

CONCLUSION: For this iteration, the Logistic Regression algorithm achieved the best overall results using the training and testing datasets. For this dataset, Logistic Regression should be considered for further modeling or production use.

Dataset Used: Heart Disease Data Set

Dataset ML Model: Binary classification with numerical and categorical attributes

Dataset Reference:

One potential source of performance benchmark:

The HTML formatted report can be found here on GitHub.