5 research outputs found

    An In-Depth Comparative Analysis of Machine Learning Techniques for Addressing Class Imbalance in Mental Health Prediction.

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    The application of machine learning (ML) in predicting mental healthcare faces a challenge due to imbalanced datasets. ML techniques analyse extensive datasets to make predictions; however, the unequal distribution of samples, with the majority belonging to diagnosed mental disorders, can lead to biased model training and limited generalisation. To mitigate the issue of class imbalance in mental health datasets, this study employed diverse ML techniques, namely, resampling, ensemble, and algorithm-specific approaches and metrics such as accuracy, precision, recall and F1 score. The dataset used was collected from the Open Sourcing Mental Illness website, spanning 2016 to 2021. The findings indicate that ensemble techniques, particularly Random Forest, excelled in managing class imbalance compared to other methods. Beyond conventional performance metrics, the study introduced Kappa, balanced accuracy, and geometric mean to evaluate model effectiveness. These findings provide valuable insights for improving mental health predictions, enabling early diagnosis and personalised treatment strategies

    Multidisciplinarity in Data Science Curricula

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    This paper sought to identify and compare disciplinary emphases in data science curricula across South Africa’s 26 public universities using a website scoping review method. The key findings reveal that only 12 of the 26 universities offer data science programmes that are publicly accessible on their websites. Of those 12, only 5 offer data science at the undergraduate level, and these undergraduate programmes are objectified (entirely leaning) to the science, technology, engineering, and mathematics (STEM) disciplines. Only seven of the universities offer a few non-STEM subjects with only one offering more non-STEM subjects compared to STEM subjects. The implications are that curricula of data science, which is multidisciplinary in nature, are more likely to inherit the STEM curricula challenges. The resultant impact will therefore likely extend to skills, future careers, and employment, in view of the growing demand for data scientists amid the unemployment challenges. It is recommended that intentional efforts must be made to necessarily ideologise non-STEM disciplines into data science curricula in South Africa, that is, to deeply embed societal contexts into data science curricula