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Journal : Indonesian Journal of Electrical Engineering and Computer Science

Expert systems in mental health: innovative approach for personalized treatment Andrade-Arenas, Laberiano; Rubio-Paucar, Inoc; Celis, Domingo Hernández; Yactayo-Arias, Cesar
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp414-427

Abstract

Custom classification of mental illnesses has emerged as a challenge for mental health specialists, often minimized by patients' lack of awareness of symptoms and the importance of early intervention. Therefore, the purpose of this research is to provide a comprehensive understanding of personalized treatment, encompassing both pharmacological and non-pharmacological options, specifically tailored to mental disorders, considering factors such as the patient's age and gender, among other relevant characteristics. In this context, the Buchanan methodology has been chosen as the framework for structuring a web-based expert system. This approach covers everything from problem identification to system implementation and subsequent evaluation. The survey results, with a total of 50 responses, reveal that the category "Good" leads with 70%, closely followed by "Fair" and "Poor," both at 14%. 71.4% of responses reflect a positive evaluation, with 85.7% combining "Good" or "Fair" responses, and all categories reaching 100%. These results support the feasibility and effectiveness of implementing a web-based expert system under the Buchanan methodology. A positive response in the survey suggests that this methodology can significantly contribute to personalizing and recommending appropriate treatments, both pharmacological and non-pharmacological, thereby benefiting a broad spectrum of patients with mental disorders.
Diagnosis and treatment of Guillain-Barre using the prolog expert system Andrade-Arenas, Laberiano; Molina-Velarde, Pedro; Pucuhuayla-Revatta, Félix; Yactayo-Arias, Cesar

Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i1.pp333-342

Abstract

This research is mostly about Guillain-Barre syndrome (GBS), a complicated neurological condition with many subtypes that make diagnosis and treatment hard, even though medical care is always getting better. The main goal of this study is to build and test an expert system that can correctly diagnose these subtypes, with a focus on early detection and personalized treatments. The evaluation of the system was carried out using a dataset composed of 20 cases (12 positive and 8 negative). A confusion matrix was used to evaluate key metrics such as precision, sensitivity, and specificity. The findings demonstrate precision and sensitivity of 83% and specificity of 75%. These findings unambiguously demonstrate the efficacy of the system in correctly identifying positive Guillain-Barre cases while substantially reducing false negatives. In conclusion, this expert system offers a potentially useful tool to improve the accuracy of the diagnosis and treatment of Guillain-Barre patients. However, to take advantage of its full potential in clinical practice, it should be used as diagnostic support and not replace the medical staff, and it should be updated periodically to reflect new findings in medicine.