Predictive model for cerebrovascular disease risk factors using ant-miner algorithm
Keywords:
predictive model, cerebrovascular disease, ant-miner algorithmAbstract
Stroke remains a leading cause of mortality and disability worldwide, with a significant impact on public health. Early accurate risk assessment and diagnosis are crucial for effective prevention and treatment strategies. This study aimed to analyze medical data of cerebrovascular disease from an international database, extract beneficial risk factors related to diagnosis, and create an advanced model for stroke diagnosis using data mining techniques. This study was analyzed
data from the Stroke Prediction Dataset, comprising 5,110 patient records with 11 clinical attributes. Multivariate logistic regression analysis was employed to identify significant risk factors. The Ant-Miner Algorithm, inspired by ant colony behavior, was utilized to develop a diagnostic model for stroke. The model's performance was evaluated using 5-fold cross-validation and compared with other machine learning techniques, including decision trees and random forest. The results found that identified age over 65 years (OR = 3.72, 95% CI = 2.86-4.84), hypertension (OR = 2.98, 95% CI = 2.31-3.84), heart disease (OR = 2.75, 95% CI = 2.07-3.66), and high blood glucose levels (OR = 2.45, 95% CI = 1.89-3.17 for >150 mg/dL) as the most significant risk factors for stroke. The Ant-Miner Algorithm-based model excelled in handling imbalanced medical data, generating interpretable clinical rules high and valuable for clinical decisions. It achieved 98.24% accuracy, 92.00% precision, 90.20% sensitivity, and 91.09% F1-score. These explainable rules, like “IF (age>65) AND (avg_glucose>150) AND (hypertension=1) THEN high stroke risk with OR = 27.12”, are applicable in real-world stroke diagnostics. In conclusion, the Ant-Miner algorithm model proved to be an accurate and stroke diagnostic model which simply identifies risk factors and diagnostic rules that can aid in early detection and prevention of stroke, potentially improving clinical decision-making through this data mining innovation. Further studies are recommended to validate the model's performance in other diseases of diverse populations and to explore its potential applications in clinical practice and public health interventions. The integration of this model with electronic health records and the development of user-friendly applications for healthcare professionals could significantly enhance stroke risk assessment and management tools.
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