RESUMEN
The cognitive impairment known as dementia affects millions of individuals throughout the globe. The use of machine learning (ML) and deep learning (DL) algorithms has shown great promise as a means of early identification and treatment of dementia. Dementias such as Alzheimer's Dementia, frontotemporal dementia, Lewy body dementia, and vascular dementia are all discussed in this article, along with a literature review on using ML algorithms in their diagnosis. Different ML algorithms, such as support vector machines, artificial neural networks, decision trees, and random forests, are compared and contrasted, along with their benefits and drawbacks. As discussed in this article, accurate ML models may be achieved by carefully considering feature selection and data preparation. We also discuss how ML algorithms can predict disease progression and patient responses to therapy. However, overreliance on ML and DL technologies should be avoided without further proof. It's important to note that these technologies are meant to assist in diagnosis but should not be used as the sole criteria for a final diagnosis. The research implies that ML algorithms may help increase the precision with which dementia is diagnosed, especially in its early stages. The efficacy of ML and DL algorithms in clinical contexts must be verified, and ethical issues around the use of personal data must be addressed, but this requires more study.
RESUMEN
The present study aimed to study the impact of neurofeedback on the academic performance of nursing students with academic failure. Methods: This study was an experimental one with a pretest-posttest design with a control group. The statistical population of this research was the nursing students of the Faculty of Nursing, Tehran University of Medical Sciences University of Medical Sciences. The sample of this study consisted of 60 individuals chosen by a simple random sampling method and two experiment groups (N=30) and a control group (N=30) were replaced by accident. Neurofeedback was an advanced Raven test and a researcher-made questionnaire for data collection. Thereafter, the experimental group was treated with neurofeedback for 7-10 weeks and 20 50-min therapeutic sessions as the experimental condition. In the first 130 s, the baseline was determined for the individual, and during the session, the baseline was practiced. Each session consisted of six exercises, each lasting 7 min. Results: The results of the covariance analysis showed that students who had an educational drop and were trained in neurofeedback sessions showed a significant increase in the next half (P<0.05) compared to the control group. Conclusion: The results of this study showed that neurofeedback is an effective method for managing the academic performance of nursing students with academic failure.
