A Comparative Analysis of Ten Classifiers and their Impact on Early Detection of Stroke Prediction

Stroke represents a significant global health challenge, leading to substantial disability and mortality across the world. Countries in the developing regions, including India, bear a substantial burden of stroke cases, with the most prevalent type being ischemic stroke. The ability to predict the likelihood of stroke is of utmost importance for effective prevention and early intervention. Therefore, this conference paper aims to assess and compare the performance of various machine learning algorithms in predicting stroke. The study employs a dataset containing diverse input variables, such as age, blood pressure, diabetes status, and smoking habits. To achieve this, ten machine learning classifiers, namely Logistic Regression, K-Nearest Neighbors, Support Vector Machines, Gaussian Naive Bayes, Decision Tree, Random Forest, XGBoost, Stochastic Gradient Descent, and AdaBoost are implemented and evaluated for their predictive capabilities. The outcomes of this research offer valuable insights into the effectiveness of each algorithm, serving as a valuable reference for future investigations in the field of stroke prediction.