Gastrointestinal problem among Indian adults: Evidence from longitudinal aging study in India 2017-18

Impact of Gastroesophageal Reflux Disease (GERD) Symptoms on the Lifestyle and Academic Performance of Medical Students at King Faisal University

Background A number of symptoms and complications are associated with gastroesophageal reflux disease (GERD), which originates when stomach contents are refluxed into the esophagus. GERD has been associated with quality of life (QoL) issues as well as health-related problems. However, the evidence of this correlation among medical students is still unproven. This study aims to assess GERD severity and symptoms among medical students and investigate the effects of GERD on academic performance and quality of life among Saudi Arabian medical students. Methods This is a cross-sectional study design based on questionnaires distributed among medical students at King Faisal University, Saudi Arabia: Reflux-Qual Short form (RQS) and Frequency Scale for the Symptoms of GERD (FSSG). Demographic data include gender, academic year, and Grade Point Average (GPA). The participants included 382 studentsrandomly selected as the sample size, with a precision of 5% and a 95% confidence interval (CI). Results Data were collected from 382 medical students of King Faisal University. A total of 382 participants (215 (56.3%) females and 167 (43.7%) males) were evaluated. Among 382 students,325 (85.1%) were negative for GERD, while 57 (14.9%) had symptoms of GERD. In this study, the most frequent symptoms were feeling full while eating meals (8.9%), feeling of heaviness after meals (6.5%), bloating of the stomach (3.7%), and burping (3.7%). The most affected life domains were life satisfaction (18.1%), enjoyment of food (8.4%), avoidance of large meals (6.3%), and worrying about digestive problems (5.8%). This study shows a significant relationship between GERD and GPA (P < 0.005) and a significant negative relationship between GERD and QoL (P < 0.001). Conclusions GERD significantly affects the quality of life for medical students, primarily those with high GPAs. More research is needed to determine the reason behind this.

GIT-Net: An Ensemble Deep Learning-Based GI Tract Classification of Endoscopic Images

This paper presents an ensemble of pre-trained models for the accurate classification of endoscopic images associated with Gastrointestinal (GI) diseases and illnesses. In this paper, we propose a weighted average ensemble model called GIT-NET to classify GI-tract diseases. We evaluated the model on a KVASIR v2 dataset with eight classes. When individual models are used for classification, they are often prone to misclassification since they may not be able to learn the characteristics of all the classes adequately. This is due to the fact that each model may learn the characteristics of specific classes more efficiently than the other classes. We propose an ensemble model that leverages the predictions of three pre-trained models, DenseNet201, InceptionV3, and ResNet50 with accuracies of 94.54%, 88.38%, and 90.58%, respectively. The predictions of the base learners are combined using two methods: model averaging and weighted averaging. The performances of the models are evaluated, and the model averaging ensemble has an accuracy of 92.96% whereas the weighted average ensemble has an accuracy of 95.00%. The weighted average ensemble outperforms the model average ensemble and all individual models. The results from the evaluation demonstrate that utilizing an ensemble of base learners can successfully classify features that were incorrectly learned by individual base learners.