Machine Learning as a Tool to the Diagnosis of Diabetes

Authors

  • Mr. Yosef Granillo John Hopkins University Center for Talented Youth
  • Guillermo H. Goldsztein Georgia Institute of Technology

DOI:

https://doi.org/10.47611/jsrhs.v11i1.2513

Keywords:

machine learning, computer science, diabetes

Abstract

Machine learning is the field of computer science that uses data to make predictions and decisions. The problem we consider in this article belongs to the class known as supervised learning and the technique we use is logistic regression. After explaining supervised learning and logistic regression, we use a data set to develop a computational model able to give a diabetes diagnosis to patients. We discuss the accuracy of the model developed.

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Author Biography

Guillermo H. Goldsztein, Georgia Institute of Technology

Mentor

References or Bibliography

Ethem Alpaydin. 2010. Introduction to Machine Learning (2nd ed.). MIT Press.

Andreas C. Müller, Sarah Guido, 2016. Introduction to Machine Learning with Python. O'Reilly Media, Inc.

Mehryar Mohri, Afshin Rostamizadeh and Ameet Talwalkar, 2018, Foundations of Machine Learning (2nd ed.).

MIT Press.

Kevin P. Murphy, 2012, Machine Learning A Probabilistic Perspective, MIT Press.

https://www.kaggle.com/alexteboul/diabetes-health-indicators-dataset?select=diabetes_binary_5050split_health_indicators_BRFSS2015.csv

Published

02-28-2022

How to Cite

Granillo, Y., & Goldsztein, G. H. (2022). Machine Learning as a Tool to the Diagnosis of Diabetes. Journal of Student Research, 11(1). https://doi.org/10.47611/jsrhs.v11i1.2513

Issue

Vol. 11 No. 1 (2022)

Section

HS Research Articles

Copyright (c) 2022 Mr. Yosef Granillo; Guillermo H. Goldsztein

Creative Commons License

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