Rehmani Filza, Shaheen Qaisar, Anwar Muhammad, Faheem Muhammad, Bhatti Shahzad Sarwar
Department of Computer Science & Information Technology The Islamia University of Bahawalpur Bannu Pakistan.
Department of Information Sciences, Division of Science and Technology University of Education Lahore Pakistan.
Healthc Technol Lett. 2024 Jun 10;11(4):218-226. doi: 10.1049/htl2.12088. eCollection 2024 Aug.
Depression is a serious mental state that negatively impacts thoughts, feelings, and actions. Social media use is rapidly growing, with people expressing themselves in their regional languages. In Pakistan and India, many people use Roman Urdu on social media. This makes Roman Urdu important for predicting depression in these regions. However, previous studies show no significant contribution in predicting depression through Roman Urdu or in combination with structured languages like English. The study aims to create a Roman Urdu dataset to predict depression risk in dual languages [Roman Urdu (non-structural language) + English (structural language)]. Two datasets were used: Roman Urdu data manually converted from English on Facebook, and English comments from Kaggle. These datasets were merged for the research experiments. Machine learning models, including Support Vector Machine (SVM), Support Vector Machine Radial Basis Function (SVM-RBF), Random Forest (RF), and Bidirectional Encoder Representations from Transformers (BERT), were tested. Depression risk was classified into not depressed, moderate, and severe. Experimental studies show that the SVM achieved the best result with anaccuracy of 0.84% compared to existing models. The presented study refines thearea of depression to predict the depression in Asian countries.
抑郁症是一种严重的精神状态，会对思想、情感和行为产生负面影响。社交媒体的使用正在迅速增长，人们用各自的地区语言来表达自己。在巴基斯坦和印度，许多人在社交媒体上使用罗马乌尔都语。这使得罗马乌尔都语对于预测这些地区的抑郁症具有重要意义。然而，先前的研究表明，通过罗马乌尔都语或与英语等结构化语言结合来预测抑郁症并没有显著贡献。该研究旨在创建一个罗马乌尔都语数据集，以预测双语[罗马乌尔都语（非结构化语言）+英语（结构化语言）]环境下的抑郁症风险。使用了两个数据集：从脸书上手动从英语转换而来的罗马乌尔都语数据，以及来自Kaggle的英语评论。这些数据集被合并用于研究实验。对包括支持向量机（SVM）、支持向量机径向基函数（SVM - RBF）、随机森林（RF）和双向编码器表征从变换器（BERT）在内的机器学习模型进行了测试。抑郁症风险被分类为无抑郁、中度抑郁和重度抑郁。实验研究表明，与现有模型相比，支持向量机取得了最佳结果，准确率为0.84%。本研究细化了抑郁症预测领域，以预测亚洲国家的抑郁症情况。
