Investigating differential linguistic patterns exhibited by Major Depressive Disorder (MDD) Patients and building a Long Short Term Memory Network + Convolutional Neural Network Model, Logistic Regression model, and a Multinomial Naive Bayes Classifier Algorithm to develop Spero, a hybrid app based Early-MDD diagnosis system - Garg, Shivam; Raigosa, Ashley; Aiman, Rimsha (International Journal of Scientific Research in Computer Science Engineering and Information Technology, 2020)
Major Depressive Disorder (MDD), otherwise known as Depression, is the leading psychiatric disorder globally in terms of the number of individuals it affects. Despite this there is no effective and reliable early diagnostics system for MDD. Hence, through this study, we aimed to fill this void by not only investigating linguistic differences in posts made on social media by people exhibiting and people not exhibiting symptoms of MDD but also by developing various machine learning architectures to build an accessible, sensitive, and accurate MDD early diagnostics system. Through the differential linguistic analysis we conducted on the dataset we manually scraped and filtered, we clearly demonstrated that there indeed were certain linguistic and topical features that were different amongst depressed and healthy patients. Furthermore, we also successfully built three different ML Algorithms in which our Long Short Term Memory Network (LSTM) + Convolutional Neural Network (CNN) Model attained an accuracy of 95.00%, our Multinomial Naive Bayes Classifier Algorithm attained an accuracy of 92%, and our Logistic Regression Model achieved an accuracy of 87.627%. Ultimately, given the LSTM + CNN Model’s high accuracy, weighted precision (0.95), recall (0.95), and f-1 score (0.95), we decided to integrate it into an app built on Swift UI to develop Spero, a first of its kind early diagnostics system for MDD.