Stroke Prediction using Machine Learning

Abstract

Stroke is a leading cause of mortality and long-term disability worldwide. Early prediction of stroke risk is crucial for implementing preventive measures and reducing healthcare burdens. This study presents a comprehensive machine learning approach to predict stroke occurrence by analyzing pertinent health and demographic factors. Utilizing a publicly available dataset, we developed and evaluated multiple classification models, including Decision Tree, Random Forest, and Logistic Regression. The models were assessed based on accuracy, precision, recall, and F1-score. Our findings indicate that the Random Forest classifier achieved superior performance, demonstrating its potential utility in clinical settings for stroke risk assessment.

Overview

Stroke, characterized by the sudden loss of brain function due to an interruption in blood supply, poses significant health challenges globally. Timely identification of individuals at elevated risk enables the implementation of preventive strategies, thereby mitigating adverse outcomes. In recent years, machine learning techniques have been increasingly employed to enhance predictive accuracy in medical diagnoses. This project focuses on developing an accurate machine learning model for predicting stroke risk. It offers practical implementation of the model, aiding researchers, data scientists, and enthusiasts in understanding data preprocessing, feature engineering, model training, and evaluation.

Methodology

The project involves data analysis and preprocessing, exploratory data analysis (EDA), feature engineering, model building, and evaluation. Various machine learning models such as Decision Tree, Random Forest, and XGBoost are implemented and compared to identify the most effective model for stroke prediction.

Results

The final model was selected based on its ability to balance sensitivity and specificity, making it suitable for medical predictions. The repository includes the evaluation metrics and a detailed comparison of model performances.

BibTeX
                
                  @article{your2025stroke,

                    title={Stroke Prediction using Machine Learning},

                    author={Umair Akram},

                    year={2023}

                  }