An Enhanced Classification Model for Depression Detection Based on Machine Learning with Feature Selection Technique

Facebook, Twitter, and Instagram are just a few examples of how social media have changed our lives. People are more linked than ever before, leading to the development of a distinct online identity. Recent studies have revealed that an increased number of hours spent on social media platforms is connected with an increased likelihood of developing depression. Depression is characterized by pervasive sadness and a general absence of interest in most activities. Severe depression, often known as major depressive disorder, is a serious mental illness that can have far-reaching effects. The purpose of this study is to analyze depression, utilizing a variety of socio-demographic and psychological data to determine if a person is depressed or not. Different operations have been performed, including data collection, preprocessing, feature selection, classification, and evaluation. This research is evaluated on the depression detection dataset. Data is processed in the data preprocessing step by checking null and missing values and performing data encoding using a label encoder. Further, the recursive feature elimination technique has extracted the most important features from the dataset in the feature selection. On the other hand, machine learning-based SVM and DT techniques are used for classification. The performance of these models is measured using different performance metrics. After applying these methods, the proposed decision tree model obtains the highest 98% accuracy, which is better than the other models.