PREDICTION OF DEPRESSION USING MACHINE LEARNING TOOLS TAKING CONSIDERATION OF OVERSAMPLING
Keywords:Depression, Machine Learning, Classification, Accuracy, oversampling
Depression is a psychiatric condition characterized by a persistent sense of sadness and dullness. It is also known as a severe burdensome problem or clinical sorrow, and it impacts how a person feels, thinks, and behaves and triggers a slew of emotional and physical issues. Various components are liable for this issue, and many related sicknesses are expanding because of this infection. It is not just at risk for well-being perils, yet also produces perilous social offense, like self-destruction and family misuse. In this study, we used machine learning methods such as Random Forest (RF), Logistic Regression (LR), and Naive Bayes (NB). We also used accuracy, precision, recall, and F1-score to survey the exhibition assessment of arrangement results. These machine learning algorithms developed and analyzed Confusion matrices through data augmentation to assess the classification performance. This study used machine learning technologies to predict depression and revealed the significance of the trait. Then we have tried to utilize an oversampling technique that shows the distinction in model execution. Indeed, we wanted to see how well the recommended machine learning algorithms performed before and after rebalancing standardized data. In our suggested framework, the RF classifier performed better with 89% accuracy and 90% precision than other models.
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