Stroke Prediction using Clinical and Social Features in Machine Learning

TL;DR

This paper compares neural networks and logistic regression models to predict stroke risk using clinical and social features, aiming to identify the most effective method to reduce false negatives.

Contribution

It evaluates and contrasts neural networks and logistic regression for stroke prediction based on lifestyle factors, providing insights into their relative effectiveness.

Findings

Neural networks outperform logistic regression in accuracy.

Logistic regression offers interpretability and simplicity.

Both models help identify high-risk individuals for stroke prevention.

Abstract

Every year in the United States, 800,000 individuals suffer a stroke - one person every 40 seconds, with a death occurring every four minutes. While individual factors vary, certain predictors are more prevalent in determining stroke risk. As strokes are the second leading cause of death and disability worldwide, predicting stroke likelihood based on lifestyle factors is crucial. Showing individuals their stroke risk could motivate lifestyle changes, and machine learning offers solutions to this prediction challenge. Neural networks excel at predicting outcomes based on training features like lifestyle factors, however, they're not the only option. Logistic regression models can also effectively compute the likelihood of binary outcomes based on independent variables, making them well-suited for stroke prediction. This analysis will compare both neural networks (dense and convolutional) and logistic regression models for stroke prediction, examining their pros, cons, and differences to develop the most effective predictor that minimizes false negatives.