Harnessing the hybrid machine learning methods for stroke risk classification

Stroke is a leading global cause of death, with 80% of cases considered preventable through early detection and awareness. This study employs artificial intelligence (AI) and machine learning (ML) to predict stroke risk using a dataset containing 5,110 records and key risk factors such as age, hypertension, and heart disease. Multiple models were evaluated, including Gradient Boosting, Hist Gradient Boosting, AdaBoost, and Decision Trees. A hybrid model integrating Hist Gradient Boosting with optimizer algorithms achieved superior performance, with accuracy, precision, recall, and F1-score all exceeding 0.985, highlighting its potential for effective stroke prediction.