Machine learning models compared for stroke risk prediction

A new arXiv paper explores the use of machine learning models, specifically neural networks and logistic regression, for predicting stroke risk. The research aims to identify the most effective predictor by comparing the performance of dense neural networks, convolutional neural networks, and logistic regression models. The goal is to motivate lifestyle changes by providing individuals with an accurate assessment of their stroke likelihood, thereby reducing false negatives. AI

IMPACT Could lead to improved early detection of stroke risk, potentially motivating lifestyle changes and reducing mortality.

RANK_REASON The cluster contains a single arXiv paper detailing research on machine learning models for a specific application. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• Aidan Chadha
• alphaXiv
• arXiv
• CatalyzeX
• convolutional neural network
• DagsHub
• Dense Neural Networks
• Gotit.pub
• Hugging Face
• logistic regression model
• Neural Networks
• ScienceCast
• United States

• paper
• model release
• other

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