Implementation of Ensemble Method in Schizophrenia Identification Based on Microarray Data
Schizophrenia is a chronic mental illness that leads the patient to hallucinations and delusions with a prevalence of 0.4% worldwide. The importance early detection of Schizophrenia is tracking the pre-syndrome of Schizophrenia during the active phase, and could reduce psychosis symptomatic. However...
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| Format: | Article |
| Language: | English |
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Ikatan Ahli Informatika Indonesia
2022-02-01
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| Series: | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| Subjects: | |
| Online Access: | http://jurnal.iaii.or.id/index.php/RESTI/article/view/3788 |
| _version_ | 1797329102789672960 |
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| author | Diya Namira Purba Fhira Nhita Isman Kurniawan |
| author_facet | Diya Namira Purba Fhira Nhita Isman Kurniawan |
| author_sort | Diya Namira Purba |
| collection | DOAJ |
| description | Schizophrenia is a chronic mental illness that leads the patient to hallucinations and delusions with a prevalence of 0.4% worldwide. The importance early detection of Schizophrenia is tracking the pre-syndrome of Schizophrenia during the active phase, and could reduce psychosis symptomatic. However, the method sometimes cannot detect the symptoms accurately. As an alternative, machine learning can be implemented on microarray data for early detection. This study aimed to implement three ensemble methods, i.e., Random Forest (RF), Adaptive Boosting (AdaBoost), and Extreme Gradient Boosting (XGBoost) to identify Schizophrenia. Hyperparameter tuning was performed to improve the performance of the models. Based on the results, we found that the model 6, which is developed by the XGBoost method, performs better than other models with the value of accuracy and F1-score are 0.87 and 0.87, respectively. |
| first_indexed | 2024-03-08T07:00:23Z |
| format | Article |
| id | doaj.art-015a6f8d2f6243d48d1139e6fc9f57c0 |
| institution | Directory Open Access Journal |
| issn | 2580-0760 |
| language | English |
| last_indexed | 2024-03-08T07:00:23Z |
| publishDate | 2022-02-01 |
| publisher | Ikatan Ahli Informatika Indonesia |
| record_format | Article |
| series | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| spelling | doaj.art-015a6f8d2f6243d48d1139e6fc9f57c02024-02-03T05:46:47ZengIkatan Ahli Informatika IndonesiaJurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)2580-07602022-02-0161646910.29207/resti.v6i1.37883788Implementation of Ensemble Method in Schizophrenia Identification Based on Microarray DataDiya Namira Purba0Fhira Nhita1Isman Kurniawan2Telkom UniversityTelkom UniversityUniversitas TelkomSchizophrenia is a chronic mental illness that leads the patient to hallucinations and delusions with a prevalence of 0.4% worldwide. The importance early detection of Schizophrenia is tracking the pre-syndrome of Schizophrenia during the active phase, and could reduce psychosis symptomatic. However, the method sometimes cannot detect the symptoms accurately. As an alternative, machine learning can be implemented on microarray data for early detection. This study aimed to implement three ensemble methods, i.e., Random Forest (RF), Adaptive Boosting (AdaBoost), and Extreme Gradient Boosting (XGBoost) to identify Schizophrenia. Hyperparameter tuning was performed to improve the performance of the models. Based on the results, we found that the model 6, which is developed by the XGBoost method, performs better than other models with the value of accuracy and F1-score are 0.87 and 0.87, respectively.http://jurnal.iaii.or.id/index.php/RESTI/article/view/3788keywords: ensemble method, microarray, schizophrenia, disease detection |
| spellingShingle | Diya Namira Purba Fhira Nhita Isman Kurniawan Implementation of Ensemble Method in Schizophrenia Identification Based on Microarray Data Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) keywords: ensemble method, microarray, schizophrenia, disease detection |
| title | Implementation of Ensemble Method in Schizophrenia Identification Based on Microarray Data |
| title_full | Implementation of Ensemble Method in Schizophrenia Identification Based on Microarray Data |
| title_fullStr | Implementation of Ensemble Method in Schizophrenia Identification Based on Microarray Data |
| title_full_unstemmed | Implementation of Ensemble Method in Schizophrenia Identification Based on Microarray Data |
| title_short | Implementation of Ensemble Method in Schizophrenia Identification Based on Microarray Data |
| title_sort | implementation of ensemble method in schizophrenia identification based on microarray data |
| topic | keywords: ensemble method, microarray, schizophrenia, disease detection |
| url | http://jurnal.iaii.or.id/index.php/RESTI/article/view/3788 |
| work_keys_str_mv | AT diyanamirapurba implementationofensemblemethodinschizophreniaidentificationbasedonmicroarraydata AT fhiranhita implementationofensemblemethodinschizophreniaidentificationbasedonmicroarraydata AT ismankurniawan implementationofensemblemethodinschizophreniaidentificationbasedonmicroarraydata |