From Data to Hope: Deep Neural Network-Based Prediction of Poisoning (DNNPPS) Suicide Cases
The success of the DNN algorithm depends on the quality and quantity of data, as well as the model's architecture. High-quality data should be accurate, representative, and relevant, while a large dataset enables the DNN to learn more features. In our study, the DNNPPS model performed well, achieving an F1-score of 91%, which indicates high accuracy in predicting suicide cases and a good balance between precision and recall.
Authors
- Kambiz Bahaadinbeigy (ORCID: https://orcid.org/0000-0002-5430-3758)
- Shabnam Sadeghi Esfahlani (ORCID: https://orcid.org/0000-0002-1443-0330)
- Fahimeh Ghasemian (ORCID: https://orcid.org/0000-0002-2176-7089)
- Hassan Shirvani (ORCID: https://orcid.org/0009-0001-7045-5735)
- Houriyeh Ehtemam (ORCID: https://orcid.org/0000-0002-2375-6447)
- Mohammad Mehdi Ghaemi (ORCID: https://orcid.org/0000-0001-6427-2740)
- Alireza Sanaei (ORCID: https://orcid.org/0000-0003-2718-9678)
Institutions
- Shahid Bahonar University of Kerman (IR)
- Kerman University of Medical Sciences (IR)
- Anglia Ruskin University (GB)
Publication Details
- Journal
- Iranian Journal of Public Health
- Published
- 2024-12-23
- DOI
- https://doi.org/10.18502/ijph.v53i12.17332
- Primary Topic
- Traumatic Brain Injury Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00