Abstract
Background. Imposter syndrome (IS), associated with self‐doubt and fear despite clear accomplishments and competencies, is frequently detected in medical students and has a negative impact on their well‐being. This study aimed to predict the students’ IS using the machine learning ensemble approach. Methods. This study was a cross‐sectional design among medical students in Bangladesh. Data were collected from February to July 2020 through snowball sampling technique across medical colleges in Bangladesh. In this study, we employed three different machine learning techniques such as neural network, random forest, and ensemble learning to compare the accuracy of prediction of the IS. Results. In total, 500 students completed the questionnaire. We used the YIS scale to determine the presence of IS among medical students. The ensemble model has the highest accuracy of this predictive model, with 96.4%, while the individual accuracy of random forest and neural network is 93.5% and 96.3%, respectively. We used different performance matrices to compare the results of the models. Finally, we compared feature importance scores between neural network and random forest model. The top feature of the neural network model is Y7, and the top feature of the random forest model is Y2, which is second among the top features of the neural network model. Conclusions. Imposter syndrome is an emerging mental illness in Bangladesh and requires the immediate attention of researchers. For instance, in order to reduce the impact of IS, identifying key factors responsible for IS is an important step. Machine learning methods can be employed to identify the potential sources responsible for IS. Similarly, determining how each factor contributes to the IS condition among medical students could be a potential future direction.
Highlights
Imposter syndrome (IS) is defined by a sense of not belonging, of being out of place, and of believing that one’s perceived competence and success are underserved by others
By utilizing the features obtained from random forest (RF) and artificial neural network (ANN), we train and test the final ensemble model. is type of approach is not thoroughly explored in the current literature that we have found so far. e results validate the improved performance of our proposed technique employing the four commonly used performance metrics, namely accuracy, precision, recall, and F-1 score. erefore, the major contributions of this study can be summarized as follows: (i) Populating a real-life dataset from the Bangladeshi medical students for predicting young imposter syndrome (ii) Utilizing ensemble learning to predict IS among students in a course or school year, forecast whether a student is likely to suffer from IS
E Young Imposter Syndrome (YIS) [9] scale was used to determine whether the person had IS or not. e scale was in the form of questions, and a student was considered to have imposter syndrome if they answered “Yes” to five or more of them. e YIS scale contains a total of eight items, and if a student scored on the YIS scale, we considered him/her having IS otherwise, and he/she does not have IS
Summary
Imposter syndrome (IS) is defined by a sense of not belonging, of being out of place, and of believing that one’s perceived competence and success are underserved by others. This is regarded as a personal issue that should be addressed by keeping a record of accomplishments to serve as a reminder of progress [1,2,3]. We employed three different machine learning techniques such as neural network, random forest, and ensemble learning to compare the accuracy of prediction of the IS. We compared feature importance scores between neural network and random forest model. Determining how each factor contributes to the IS condition among medical students could be a potential future direction
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