Stroke prediction using machine learning models

Junyi Li

doi:10.1117/12.2692928

7 September 2023 Stroke prediction using machine learning models

Junyi Li

Proceedings Volume 12789, International Conference on Modern Medicine and Global Health (ICMMGH 2023); 127891I (2023) https://doi.org/10.1117/12.2692928
Event: International Conference on Modern Medicine and Global Health (ICMMGH 2023), 2023, Oxford, United Kingdom

Abstract

Stroke is a medical emergency that occurs when blood flow to a part of the brain is interrupted or when a blood vessel in the brain ruptures. It is the fifth leading cause of death and a significant cause of disability in the United States. Certain health conditions and lifestyles increase the risk of stroke, including high blood pressure, high cholesterol, heart disease, obesity, and diabetes. This paper presents a stroke prediction system based on various health conditions and other attributes using 7 machine learning models including random forest, kneighbors and so on. The methodology section outlines data collection, processing, and machine learning model selection and evaluation metrics. The results and discussion section presents detailed analysis and results from exploratory data analysis and machine learning classifiers. Finally, the conclusion section summarizes the paper and provides future directions for the project. The study aims to contribute to the prevention of stroke and improve healthcare outcomes for patients.

Citation Download Citation

Junyi Li "Stroke prediction using machine learning models", Proc. SPIE 12789, International Conference on Modern Medicine and Global Health (ICMMGH 2023), 127891I (7 September 2023); https://doi.org/10.1117/12.2692928

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