Predicting Stroke Risk Based on an Optimized Machine Learning Model

Stroke risk prediction is critical for identifying at-risk populations in healthcare as well as for early diagnosis and resource optimization in stroke management. This study's main goal was to create a reliable stroke prediction model by analyzing a publically accessible dataset containing 4981 records and 11 variables using machine learning techniques. Initial data preprocessing consisted of converting categorical variables to numerical values and addressing class imbalance through methods such as class weighting and the Synthetic Minority Oversampling Technique (SMOTE). Three basic models - logistic regression, decision trees, and random forests - were implemented to establish base performance. To improve prediction accuracy and address imbalances, optimized ensemble models were built using stacking and hyperparameter tuning through grid search and cross-validation. This paper evaluates the performance of the model based on composite indicators. While the base model demonstrated high accuracy and recall, the optimized model had a superior performance with an AUC score of 0.94, showing that the capacity to distinguish between stroke and non-stroke cases has significantly improved. By offering a useful predictive tool that can precisely estimate the risk of stroke and direct creative ways to early intervention, this study advances the field of healthcare analytics.