Implementation of Ensemble Method in Schizophrenia Identification Based on Microarray Data
Abstrak
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.
Topik & Kata Kunci
Penulis (3)
Diya Namira Purba
Fhira Nhita
Isman Kurniawan
Akses Cepat
- Tahun Terbit
- 2022
- Sumber Database
- DOAJ
- DOI
- 10.29207/resti.v6i1.3788
- Akses
- Open Access ✓