Name
Title | ||
|---|---|---|
Adaptive mining prediction model for content recommendation to coronary heart disease patients |
Abstract | ||
|---|---|---|
This paper proposes the Fuzzy Rule-based Adaptive Coronary Heart Disease Prediction Support Model (FbACHD_PSM), which gives content recommendation to coronary heart disease patients. The proposed model uses a mining technique validated by medical experts to provide recommendations. FbACHD_PSM consists of three parts for heart disease risk prediction. First, a fuzzy membership function is constructed using medical guidelines and statistical methods. Then, a decision-tree rule induction technique creates mining-based rules that are subjected to validation by medical experts. As the rules may not be medically suitable, the experts add rules that have been verified and delete inappropriate rules. Thirdly, using fuzzy inference based on Mamdani's method, the model predicts the risk of heart disease. Based on this, final recommendations are provided to patients regarding normal living, nutrition control, exercise, and drugs. To implement our proposed model and evaluate its performance, we use a dataset from a single tertiary hospital. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/s10586-013-0308-1 | Cluster Computing |
Keywords | Field | DocType |
fbachd_psm,fuzzy logic,coronary heart disease,data mining,decision tree | Decision tree,Data mining,Computer science,Fuzzy logic,Fuzzy inference,Fuzzy membership function,Heart disease risk,Rule induction,Fuzzy rule,Heart disease | Journal |
Volume | Issue | ISSN |
17 | 3 | 1573-7543 |
Citations | PageRank | References |
16 | 1.07 | 11 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jaekwon Kim | 1 | 164 | 27.56 |
| Jong Sik Lee | 2 | 74 | 18.95 |
| Dong-kyun Park | 3 | 16 | 3.44 |
| Yong-Soo Lim | 4 | 16 | 1.07 |
| Young-Ho Lee | 5 | 117 | 11.50 |
| Eun-Young Jung | 6 | 82 | 6.55 |