Predictors of high school dropout, anxiety, and depression in genetic generalized epilepsy

Using machine learning approach to predict depression and anxiety among patients with epilepsy in China: A cross-sectional study

BACKGROUND

Anxiety and depression are the most prevalent comorbidities among epilepsy patients. The screen and diagnosis of anxiety and depression are quite important for the management of patients with epilepsy. In that case, the method for accurately predicting anxiety and depression needs to be further explored.

METHODS

A total of 480 patients with epilepsy (PWE) were enrolled in our study. Anxiety and Depressive symptoms were evaluated. Six machine learning models were used to predict anxiety and depression in patients with epilepsy. Receiver operating curve (ROC), decision curve analysis (DCA) and moDel Agnostic Language for Exploration and eXplanation (DALEX) package were used to evaluate the accuracy of machine learning models.

RESULTS

For anxiety, the area under the ROC curve was not significantly different between models. DCA revealed that random forest and multilayer perceptron has the largest net benefit within different probability threshold. DALEX revealed that random forest and multilayer perceptron were models with best performance and stigma had the highest feature importance. For depression, the results were much the same.

CONCLUSIONS

Methods created in this study may offer much help identifying PWE with high risk of anxiety and depression. The decision support system may be valuable for the everyday management of PWE. Further study is needed to test the outcome of applying this system to clinical settings.

Incorporation of Epilepsy into Life Skills Education: Perceptions of Primary School Learners in Mpumalanga and Limpopo Province—A Qualitative Exploratory Study

Providing education on epilepsy is crucial, as this helps individuals to acquire the necessary knowledge and skills to effectively manage seizures while also reducing the stigma and misconceptions surrounding the condition. The aim of this research was to examine how learners perceive the integration of epilepsy education into life skills training. A descriptive-exploratory design was utilized for the study. The research took place in the provinces of Limpopo and Mpumalanga, located in South Africa, where primary schools in chosen rural communities were selected for the research. Six focus group discussions were conducted with learners aged 9 to 14 years in grades 4 to 7. Each group was comprised of six learners, resulting in a total of 36 individuals who satisfied the inclusion criteria. Data were collected from August to November 2022. Semi-structured interviews were utilized to collect data until saturation was reached. The collected data were analyzed with the assistance of ATLAS.ti. The study’s results underscore the significance of incorporating epilepsy education into life skills curricula at the primary school level, as revealed by two prominent themes that emerged: first, the reasons provided by students for the integration of epilepsy education into life skills training, and second, the preferred teaching methodologies for epilepsy education within the life skills curriculum as identified by learners. Trustworthiness and ethical consideration were ensured. It is recommended that guidelines for epilepsy life skills should be developed to enhance the wellbeing and academic performance of learners with epilepsy in primary schools in Mpumalanga and Limpopo provinces.