Regression Model for Diabetes 130-US Hospitals Using Python and Scikit-learn

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

SUMMARY: This project aims to construct a predictive model using various machine learning algorithms and document the end-to-end steps using a template. The Diabetes 130-US Hospitals dataset is a binary classification situation where we attempt to predict one of the two possible outcomes.

INTRODUCTION: The data set is the Diabetes 130-US Hospitals for years 1999-2008 donated to the University of California, Irvine (UCI) Machine Learning Repository. The dataset represents ten years (1999-2008) of clinical care at 130 US hospitals and integrated delivery networks. It includes over 50 features representing patient and hospital outcomes.

ANALYSIS: The average performance of the machine learning algorithms achieved an accuracy benchmark of 61.20% using the training dataset. We selected the Logistic Regression and Random Forest models to perform the tuning exercises. After a series of tuning trials, the refined Random Forest model processed the training dataset with a final accuracy score of 64.38%. When we processed the test dataset with the final model, the model achieved an accuracy score of 64.61%.

CONCLUSION: In this iteration, the Random Forest model appeared to be a suitable algorithm for modeling this dataset.

Dataset Used: Diabetes 130-US Hospitals for years 1999-2008 Dataset

Dataset ML Model: Binary classification with numerical and categorical attributes

Dataset Reference:

One potential source of performance benchmarks:

The HTML formatted report can be found here on GitHub.