Machine learning-assisted prediction of associated risk factors for depression, anxiety and stress among nursing students
Keywords:
Prevalence; depression; anxiety; stress; university students; machine learning methodsAbstract
During their time as students, nursing students are exposed to numerous stressors that result in physical and mental health issues and poor academic performance. Using the machine learning method, this article investigates the depression, anxiety, and stress among Malaysian university nursing students. The subjects were assured of the secrecy and anonymity of the data collected. Multiple logistic regression and scale 21 was used to identify significant relationships between variables. The sample comprised 83.90 percent female and 16.09 percent male students. The proposed system achieves 80.3% Sensitivity, 80.5% Specificity, 89.8% Accuracy, 89.8% Precision, and 73.23 JSC (Jaccard Similarity coefficient). Therefore, the proposed system's final average CR (Classification Rate) is approximately 89.6%. In this article, the k-fold cross-validation method is utilized to cross validate the experimental results of the proposed method. According to various university level surveys, depression, anxiety, and stress affect 47.8 percent, 66.34 percent, and 36.54 percent of students. According to the findings of this study, respondents have a high
prevalence of Sp, Se, Acc, Pre, and JSC, was achieved using machine learning method.
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References
Brahima Sory keita, Ir.Pratap Nair, Haarindra Prasad, Sudhakara pandian, S.Deivasigamani, "Classification of Benign and Malignant MRIs using SVM Classifier for Brain Tumor Detection," International Journal of Engineering Trends and Technology, vol. 70, no. 3, pp. 234-240, 2022. Crossref, https://doi.org/10.14445/22315381/IJETTV70I3P226
Chatterjee, S., Saha, I., Mukhopadhyay, S., Misra, R., Chakraborty, A., & Bhattacharya, A. (2014). Depression among nursing students in an Indian government college. British Journal of Nursing, 23(6), 316–320. doi:10.12968/bjon.2014.23.6.316.
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