Research Article
Anxiety, Depression, and Health-Related Quality of Life in Family Caregivers of Patients with Epilepsy
Guven Arslan*
Issue:
Volume 11, Issue 2, April 2026
Pages:
22-34
Received:
6 July 2026
Accepted:
16 July 2026
Published:
30 July 2026
Abstract: Epilepsy is a chronic neurological disorder that imposes substantial physical, psychological, and social burdens not only on patients but also on their family caregivers. Although psychiatric comorbidities and impaired quality of life have been extensively investigated in patients with epilepsy, the psychological well-being and health-related quality of life of their caregivers remain insufficiently studied. This study aimed to evaluate anxiety, depression, and health-related quality of life among family caregivers of patients with epilepsy and to identify patient-related factors associated with these outcomes. The study included three groups: patients with epilepsy, their caregivers, and healthy controls. Differences in scale scores between two groups were analyzed using the independent-samples t-test, whereas comparisons among three or more groups were performed using one-way analysis of variance (ANOVA). Associations between categorical variables were evaluated using Pearson's chi-square test or the likelihood ratio test, depending on the distribution of the data. Pearson's correlation coefficient was calculated for continuous variables. A p-value of <0.05 was considered statistically significant. Caregivers of patients with epilepsy had significantly higher anxiety and depression scores than healthy controls. These findings indicate an increased prevalence of anxiety and depressive symptoms among caregivers. Furthermore, evaluation of quality of life using the Short Form-36 (SF-36) questionnaire demonstrated significant impairment across all quality-of-life domains in the caregiver group. The findings suggest that being a caregiver of a patient with epilepsy is, by itself, an important factor adversely affecting emotional well-being and quality of life. Changes in caregivers' mood status and quality of life were not associated with the patients' sex, seizure type, or seizure frequency.
Abstract: Epilepsy is a chronic neurological disorder that imposes substantial physical, psychological, and social burdens not only on patients but also on their family caregivers. Although psychiatric comorbidities and impaired quality of life have been extensively investigated in patients with epilepsy, the psychological well-being and health-related quali...
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Research Article
A Transparent AI-Driven Ensemble Learning Approach for Predicting Mental Health Risks among University Students in Kenya
Issue:
Volume 11, Issue 2, April 2026
Pages:
35-51
Received:
8 September 2025
Accepted:
7 October 2025
Published:
20 August 2026
Abstract: This study addresses the escalating mental health crisis among university students in Kenya, where psychological distress, driven by academic, financial, social, and transitional pressures, is increasingly prevalent yet under-prioritized in public health discourse. While global studies report distress rates exceeding 75% and Kenyan prevalence exceeds 40%, there remains a critical lack of localized, data-driven models to enable early detection in sub-Saharan African university settings. To address this gap, we developed transparent ensemble machine learning models, Random Forest (RF) and Extreme Gradient Boosting (XGBoost), to predict mental health distress levels (low, moderate, high) among 1,128 students across universities in Tharaka Nithi County, Kenya. Using a structured questionnaire incorporating demographic, academic, financial, psychosocial, and Quarter-Life Crisis (QLC) factors, we trained the models on 70% of the data and evaluated them on the remaining 30%. Both models achieved high performance: RF and XGBoost attained 98.8% accuracy (95% CI: 96.9–99.7%), Cohen’s Kappa of 0.978, and multi-class AUCs of 1.000 (RF) and ≥0.997 (XGBoost). Class-specific metrics revealed near-perfect precision, recall, and F1-scores; for high distress, both models achieved 100% sensitivity and specificity, with F1-scores of 1.000 (RF) and 0.988 (XGBoost). SHAP and feature importance analyses identified “Quarter-Life Crisis,” faculty of study (especially Science, Technology, Nursing, and Engineering), personal/mental health history, financial stress, and residence as top predictors. These findings support the use of interpretable AI for equitable, early-risk screening. However, careful attention must be paid to ethical considerations, including data privacy, potential algorithmic bias, and mental health stigma. We recommend integrating such models into university wellness platforms to enable proactive, targeted support for at-risk students.
Abstract: This study addresses the escalating mental health crisis among university students in Kenya, where psychological distress, driven by academic, financial, social, and transitional pressures, is increasingly prevalent yet under-prioritized in public health discourse. While global studies report distress rates exceeding 75% and Kenyan prevalence excee...
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