Stroke Risk Assessment Decision-Making Using a Machine Learning Model: Logistic-AdaBoost

Abstract

Stroke is a chronic cerebrovascular disease that carries a high risk. Stroke risk assessment is of great significance in preventing, reversing and reducing the spread and the health hazards caused by stroke. Aiming to objectively predict and identify strokes, this paper proposes a new stroke risk assessment decision-making model named Logistic-AdaBoost (Logistic-AB) based on machine learning. First, the categorical boosting (CatBoost) method is used to perform feature selection for all features of stroke, and 8 main features are selected to form a new index evaluation system to predict the risk of stroke. Second, the borderline synthetic minority oversampling technique (SMOTE) algorithm is applied to transform the unbalanced stroke dataset into a balanced dataset. Finally, the stroke risk assessment decision-making model Logistic-AB is constructed, and the overall prediction performance of this new model is evaluated by comparing it with ten other similar models. The comparison results show that the new model proposed in this paper performs better than the two single algorithms (logistic regression and AdaBoost) on the four indicators of recall, precision, F1 score, and accuracy, and the overall performance of the proposed model is better than that of common machine learning algorithms. The Logistic-AB model presented in this paper can more accurately predict patients' stroke risk.