발간년도 : [2022]
| 논문정보 |
|
| 논문명(한글) |
[Vol.17, No.4] A Graph Attention Network-based Heart Disease Prediction Model for Reflecting High-order Representations Between Patients |
|
| 논문투고자 |
Young-jin Kim, Hyun-jae Lee, and Ha-young Kim |
|
| 논문내용 |
Heart disease is one of the leading causes of death in modern society. Over 50% of adults have at least one of three risk factors, high blood pressure, high cholesterol and smoking, for heart disease. Also, there are other major causes including diabetes, obesity, lack of exercise and heavy drinking. Diagnosing heart disease is a complex medical problem that requires an analysis of a combination of clinical and pathological data. Meeting a need for a high level of clinical expertise for the analysis gives rise to excessive cost. Reducing the cost has become an important issue. To resolve the issue, we propose Heart Relation Graph Neural Network(HRGNN) model with a novel-designed architecture. Specifically, we design Graph Attention Network(GAT)-based heart disease prediction model with three-type clinical information and propose dual-level attention mechanism to learn high-order representation of each type and attribute. HRGNN is can learn the hidden patterns in the high-order graph structure, which is a framework considering the complex type-specific patient data correlations. Health survey data of 401,958 adults provided by the Centers for Disease Control and Prevention are used in this study. Our experiments show that HRGNN outperforms existing Graph Neural Networks(GNN) and traditional machine learning models at node classification tasks. Also, it is experimentally demonstrated that three-type branch, dual-level attention modules and data imbalance resolving methods applied to classification models improve the performance. |
|
| 첨부논문 |
|
|
|
|
|
자료실 > 논문지 
