A Novel Two-step Feature Selection based Cost Sensitive Myocardial Infarction Prediction Model
- DOI
- 10.2991/ijcis.11.1.65How to use a DOI?
- Keywords
- Cost sensitive SMO; Myocardial Infarction; Sensitivity; Two-Step Feature Selection; Prediction
- Abstract
Considering the rapid growth, complications and treatment side-effects of MI, so using data mining techniques seems necessary. On the other hand, in real-world MI cases are much less compared to healthy cases. The traditional algorithms for imbalanced problems lead to very low Sensitivity, thus, we propose a cost sensitive SMO model that utilizes a Two-Step Feature Selection, which aims to propose a model for prediction MI with regard to its imbalanced dataset to achieve a proper performance. In the dataset the MI cases in training set reduced to 9 against 410 healthy cases. After selecting 62 features, by feature selection, the Cost sensitive SMO which is allocated different misclassification cost as penalties is applied on the dataset. The results have shown positive impacts on performance.
- Copyright
- © 2018, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).
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TY - JOUR AU - Hodjat Hamidi AU - Atefeh Daraei PY - 2018 DA - 2018/01/01 TI - A Novel Two-step Feature Selection based Cost Sensitive Myocardial Infarction Prediction Model JO - International Journal of Computational Intelligence Systems SP - 861 EP - 872 VL - 11 IS - 1 SN - 1875-6883 UR - https://doi.org/10.2991/ijcis.11.1.65 DO - 10.2991/ijcis.11.1.65 ID - Hamidi2018 ER -