Diagnosing Schizophrenia
| Autor: | Sharma V. Thankachan, Varadraj P. Gurupur, Srivathsan Srinivasagopalan, Justin Barry |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
Artificial neural network business.industry Computer science Deep learning Schizophrenia (object-oriented programming) Logistic regression Machine learning computer.software_genre 030218 nuclear medicine & medical imaging Random forest Support vector machine 03 medical and health sciences 030104 developmental biology 0302 clinical medicine Artificial intelligence business computer |
| Zdroj: | BCB |
| DOI: | 10.1145/3233547.3233658 |
| Popis: | This paper presents a new method for diagnosing schizophrenia using deep learning. This experiment used a secondary dataset supplied by the National Institute of Health. The experiment analyzes the dataset and identifies schizophrenia using traditional machine learning methods such as logistic regression, support vector machines, and random forest. Finally, a deep neural network with three hidden layers is applied to the dataset. The results show that the neural network model yielded the highest accuracy, suggesting that deep learning may be a feasible method for diagnosing schizophrenia. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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