Implementation of Ensemble Method in Schizophrenia Identification
Based on Microarray Data

Implementation of Ensemble Method in Schizophrenia Identification
Based on Microarray Data

ensemble method, microarray, schizophrenia, disease detection

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.

Diya Namira Purba1
, Fhira Nhita2
, Isman Kurniawan3

Telkom University

01-02-2022

Fajar bagus W

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Indonesia

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