Stroke prediction using Ensemble Learning

Abstract

Stroke is one of the leading causes of death and long-term disability worldwide, making early prediction and preventive healthcare extremely important. Traditional methods of stroke diagnosis mainly depend on clinical observations and medical expertise, which may sometimes delay timely identification of high-risk patients. To address this challenge, the proposed project “Stroke Prediction Using Ensemble Learning” introduces an intelligent healthcare prediction system that utilizes machine learning techniques to predict the likelihood of stroke occurrence based on patient health parameters. The system analyzes important medical attributes such as age, hypertension, heart disease, glucose level, body mass index (BMI), smoking status, and other lifestyle-related factors to provide accurate stroke risk assessment. The proposed model employs Ensemble Learning techniques, which combine multiple machine learning algorithms to improve prediction accuracy and robustness. Algorithms such as Random Forest, Gradient Boosting, AdaBoost, and Voting Classifier are integrated to enhance overall system performance and reduce the limitations of individual models. Data preprocessing techniques including missing value handling, normalization, feature selection, and class balancing are applied to improve the quality of the dataset and ensure reliable predictions. The trained ensemble model is capable of identifying complex patterns in medical data and classifying patients into stroke-risk categories with high efficiency. The system is designed with a user-friendly interface that allows healthcare professionals or users to input patient information and obtain instant prediction results. Performance evaluation metrics such as accuracy, precision, recall, F1-score, and confusion matrix are used to assess the effectiveness of the proposed model. By providing early stroke risk prediction, the system can support doctors in decision-making, promote preventive healthcare, and reduce mortality rates associated with stroke. The proposed approach demonstrates how ensemble learning and artificial intelligence can contribute significantly to modern healthcare systems by enabling faster, more accurate, and cost-effective medical predictions.