Trisnawarman, Dedy and Sutrisno, Tris and Perdana, Novario Jaya and Sugesti, Sugesti Bayesian Networks Modeling Using Partial Least Squares Approach to Predict Stroke Disease. Bayesian Networks Modeling Using Partial Least Squares Approach to Predict Stroke Disease.
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Abstract
Stroke is one of the leading causes of death in Indonesia. However, the incidence of stroke continues to increase. It could affect not only old people but also young people. In Indonesia, it is estimated that 500,000 people suffer from stroke every year, and about 25% or 125,000 people die and the rest suffer minor or severe defects. This study aims to build a decision-making model to help diagnose the possibility of stroke attacks. The model combines Bayesian Networks (BNs) and Partial Least Square (PLS) methods for predicting the attack on suspected patients. The model have been tested using PLS-PM approach. Hospital medical record was used as the testing data with the help of expert verification. The results showed that the model structure of BNs built on expert assumptions can be tested using the PLS approach, and stroke disease can be predicted using interrelated indicators in the model structure of BNs.
| Item Type: | Article |
|---|---|
| Subjects: | Penelitian > Fakultas Teknologi Informasi |
| Divisions: | Fakultas Teknologi Informasi > Sistem Informasi |
| Depositing User: | Puskom untar untar |
| Date Deposited: | 04 Jan 2021 09:57 |
| Last Modified: | 04 Jan 2021 09:57 |
| URI: | http://repository.untar.ac.id/id/eprint/14184 |
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