Predicting Brain Stroke Risk Using Machine Learning: A Comprehensive Approach to Early Detection and Prevention
Year: 2025
Type: conference-paper
Abstract: Brain stroke is a medical condition where the disruption or interference of blood supply causes damage to blood cells. As a result, the effective area loses the abilities and tasks it performs. It usually affects people over the age of 25 and under 70. This research paper predicts the chances of brain stroke before it happens to save the lives of people. The dataset used in this research paper, found on the Kaggle website, has 4981 samples and 11 different features, or risk factors, on which the basis of this medical condition or event occurs. This study uses different supervised machine learning algorithms: Logistic Regression, Decision Tree, Random Forest, Naïve Bayes, and K-Nearest Neighbors (KNN). We obtained the best results from Random Forest during research, which is 94.3%.
Authors: Isha Noor, Amara Aslam, Azka Mir, Gina Purnama Insany
Institutions: University of Sialkot, Nusa Putra University
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: gold
OpenAlex ID: W4415042671