A novel medical diagnosis support system for predicting patients with atherosclerosis diseases

Abstract

Atherosclerosis diagnosis is an indistinct and complex cognitive process. Artificial intelligence methods, such as machine learning algorithms, have proven their efficiency in Medical Diagnosis Support Systems (MDSS). In this paper, we developed a novel machine learning MDSS to boost the diagnosis of cardiovascular diseases. Our study performed using 835 patient medical records that suffer from atherosclerosis, usually caused by coronary artery diseases (CAD), collected from three databases. The system input layer includes several input variables based on three databases, the Cleveland heart disease, Hungarian, and Z-Alizadeh Sani databases. Seven independent classification methods are applied to assess the system: Artificial Neural Network (ANN), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree (DT), Naïve Bayes (NB), Classification Ensemble (CE), and Discriminant Analysis (DA) algorithms. The robustness of the proposed methods was evaluated through several performance measures. The results showed that the proposed MDSS reached an accuracy of (98%), which is a higher accuracy than the existing approaches. These results are a promising step toward facilitating large-scale clinical diagnostics for atherosclerosis diseases.

Keywords

• Atherosclerosis
• Machine learning techniques
• Cardiovascular disease (CVD)
• Classification
• Prediction