PrecisionStroke: Optimized stroke risk prediction through advanced hyperparameter tuning and machine learning techniques

Abstract

Stroke prediction is a challenging problem in the healthcare domain, especially due to high class imbalance in datasets where data about stroke cases is a very small portion. In this study we develop a machine learning based framework for stroke prediction that tackles through powerful data preprocessing methods, such as Synthetic Minority Oversampling Technique (SMOTE) for balancing the classes. (Situated within the entire framework of a comparative evaluation of the Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), CatBoost, XGBoost, LightGBM, and Multi-Layer Perceptron (MLP) and TabNet machine learning classifiers as per unique designs. In conclusion, the Random Forest classifier proved to be the best performing model with an accuracy of 99.38%, followed closely by LightGBM and CatBoost. A detailed analysis including KP song metrics including precision, F1 score, and confusion matrices were carried out to ensure thorough evaluation. The study also emphasizes the importance of explainable AI tools which improve the explainability of predictions. This framework shows that machine learning has the potential to assist with early detection of stroke and may ultimately be integrated into clinical decision-making systems.