Research Article Predicting Young Imposter Syndrome Using Ensemble Learning Md. Nafiul Alam Khan , 1 M. Saef Ullah Miah , 2 Md. Shahjalal , 3 Talha Bin Sarwar , 4 and Md. Shahariar Rokon 5 1 Institute of Mathematical Sciences, Faculty of Science, University of Malaya (UM), Kuala Lumpur, Malaysia 2 FacultyofComputing,CollegeofComputingandAppliedSciences,UniversitiMalaysiaPahang(UMP),Pekan26600,Malaysia 3 Department of Public Health, North South University (NSU), Dhaka, Bangladesh 4 Department of Computer Science, Faculty of Science and Technology, American International University Bangladesh (AIUB), Dhaka, Bangladesh 5 Applied Statistics and Data Science, Department of Statistics, Jahangirnagar University (JU), Savar, Bangladesh Correspondence should be addressed to Md. Nafiul Alam Khan; nafiul.nipun95@gmail.com Received 4 September 2021; Accepted 7 January 2022; Published 21 February 2022 Academic Editor: Lingzhong Guo Copyright © 2022 Md. Nafiul Alam Khan et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 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. is study aimed to predict the students’ IS using the machine learning ensemble approach. Methods. is 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. e 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. e 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. 1. Introduction 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. Typically, this is regarded as a personal issue that should be addressed by keeping a record of accomplishments to serve as a reminder of progress [1–3]. e IS, which arbitrates the relationship be- tween perfectionism and anxiety and partially influences perfectionism and depression, was first cited by clinical psy- chologists Pauline Clance and Suzanne Imes in late 1978 [4]. According to a more recent systemic review published in 2020, the prevalence of IS in the general population ranged from 9% to 82% [5], whereas another study conducted in 2020 showed that it varied from 22% to 60% among physicians and from 33% to 40% among trainee physicians [6]. According to the current IS research in the United States, 57% of pharmacy students [7] and 15% of medical students have IS [8]. Indeed, IS is becoming a significant public health concern on a global and regional scale. For instance, the prevalence of IS among medical students has been found to be 30% in the US [9], 45.7% in Malaysia [10], and 47% in Pakistan [11]. Hindawi Complexity Volume 2022, Article ID 8306473, 10 pages https://doi.org/10.1155/2022/8306473