Purwono, Purwono and Ma’arif, Alfian and Negara, Iis Setiawan Mangku and Rahmaniar, Wahyu and Rahmawan, Jihad (2021) Linkage Detection of Features that Cause Stroke using Feyn Qlattice Machine Learning Model. Jurnal Ilmiah Teknik Elektro Komputer dan Informatika, 7 (3). pp. 423-432.
![[thumbnail of 6 Linkage Detection of Features that Cause Stroke using Feyn Qlattice Machine Learning Model.pdf]](http://eprints.uad.ac.id/style/images/fileicons/text.png) |
Text
6 Linkage Detection of Features that Cause Stroke using Feyn Qlattice Machine Learning Model.pdf Download (694kB) |
Abstract
Stroke is a disease caused by brain tissue damage because of blockage in the cerebrovascular system that disrupts body sensory and motoric systems Stroke disease is one of the highest death cause in the world. Data collection from Electronic Health Records (EHR) is increasing and has been included in the health service big data. It can be processed and analyzed using machine learning to determine the risk group of stroke disease. Machine learning can be used as a predictor of stroke causes, while the predictor clarifies the influence of each cause factor of the disease. Our contribution in this research is to evaluate Feyn Qlattice machine learning models to detect the influence of stroke disease's main cause features. We attempt to obtain a correlation between features of the stroke disease, especially on the gender as a feature, whether any other features can influence the gender feature. This research utilizes 4908 data of the disease predictor using the Feyn Qlattice model. The result implies that gender highly impacts age and hypertension on stroke disease causes. Autorun in Feyn Qlattice model was run with ten epochs, resulting in 17596 test models at 57s. Query string parameter that was focused on age and hypertension features resulted in 1245 models at 4s. An increase of accuracy was found in training metrics from 0.723 to 0.732 and in testing metrics from 0.695 to 0.708. Evaluation results showed that the model is reasonably good as a predictor of stroke disease, indicated with blue lines of AUC in training and testing metrics close to ROC's left side peak curve.
| Item Type: | Artikel Umum |
|---|---|
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisi / Prodi: | Faculty of Industrial Technology (Fakultas Teknologi Industri) > S1-Electrical Engineering (S1-Teknik Elektro) |
| Depositing User: | M.Eng. Alfian Ma'arif |
| Date Deposited: | 20 Jan 2022 04:22 |
| Last Modified: | 20 Jan 2022 04:22 |
| URI: | http://eprints.uad.ac.id/id/eprint/31769 |
Actions (login required)
 |
View Item |