A Comparative Classifier-Based Approach of an AI-Driven Clinical Decision Support System for Accurate Diagnosis of Ischemic Heart Disease
کد: G-1802
نویسندگان: Narges Norouzkhani * ℗, سرور ملوک زاده
زمان بندی: زمان بندی نشده!
برچسب: دستیار مجازی هوشمند
دانلود: دانلود پوستر
خلاصه مقاله:
خلاصه مقاله
Background and Objective: Ischemic Heart Disease (IHD) is a leading cause of mortality worldwide and is often misdiagnosed due to overlapping symptoms with other cardiovascular conditions. This study aimed to develop an Artificial Intelligence (AI)-driven Clinical Decision Support System (CDSS) to aid in the accurate diagnosis of IHD using machine learning classifiers. Materials and Methods: A retrospective dataset comprising 800 clinical records was used, with 712 cases retained after preprocessing. The data included 16 attributes such as age, sex, blood pressure, cholesterol, diabetes status, and personal habits. Attribute selection was informed by expert consultation and benchmark UCI datasets. Using Weka 3.7.0, 59 AI classification models were initially evaluated across multiple classifier families including Bayesian, tree-based, neural networks, rule-based, lazy learners, and support vector machines. Model performance was assessed using 10-fold cross-validation with sensitivity, specificity, accuracy, precision, F-measure, kappa statistics, and ROC area as key metrics. The top-performing algorithms were integrated into the CDSS prototype and evaluated against physician diagnoses and gold-standard ECG-based diagnoses. Results: The KSTAR classifier demonstrated the highest performance with an accuracy of 79.32%, sensitivity of 89%, specificity of 79%, and ROC of 0.995. Other models such as IBk, RandomTree, and MLP also exhibited high diagnostic reliability. The CDSS showed substantial agreement with physician impressions (k = 0.45, p 0.001), indicating its potential utility in real-world clinical settings. Conclusion: AI-based CDSS can significantly enhance diagnostic accuracy for IHD and support clinical decision-making, particularly in resource-limited settings.
کلمات کلیدی
Clinical Decision Support Systems, Ischemic Heart
