Preprint / Version 1

Using Machine Learning to Predict Stroke Risk

##article.authors##

  • Arnav Goel South Brunswick High School

Keywords:

Stroke, Patient, Prediction, Model, Regression, Machine Learning, Logistic

Abstract

Many things are believed to cause strokes, but the actual factors that can lead to increased risk of having a stroke can be identified using logistic regression and machine learning. Knowing these factors will allow more insight into stroke prevention.

References or Bibliography

IBM Cloud Education. “What Is Random Forest?” IBM, https://www.ibm.com/cloud/learn/random-forest#:~:text=Random%20forest%20is%20a%20commonly,both%20classification%20and%20regression%20problems.

IBM Cloud Education. “What Is Supervised Learning?” IBM, https://www.ibm.com/cloud/learn/supervised-learning.

Kumawat, Dinesh. “7 Types of Activation Functions in Neural Network.” Analytics Steps, https://www.analyticssteps.com/blogs/7-types-activation-functions-neural-network.

Kumawat, Dinesh. “Introduction to Logistic Regression - Sigmoid Function, Code Explanation.” Analytics Steps, https://www.analyticssteps.com/blogs/introduction-logistic-regression-sigmoid-function-code-explanation.

Mitchell, Tom M. Machine Learning. MacGraw-Hill, 1997.

Rossi, Richard, and Richard Rossi. Mathematical Statistics: An Introduction to Likelihood Based Inference. John Wiley & Sons, Inc., 2018.

Stroke Awareness Foundation. “Stroke Facts & Statistics.” Stroke Awareness Foundation, 23 Jan. 2021, https://www.strokeinfo.org/stroke-facts-statistics/.

Additional Files

Posted

09-16-2022