Comparative Analysis of Different Machine Learning Algorithms to Predict Depression

Depression affects people globally and requires early treatment. If a depressed individual isn’t given proper attention it leads to complications. Complications can be avoided by detecting depression earlier. To address this, Information from social media platforms and wearable technology can offer useful insights into a person’s behavior and mental condition. ML algorithms are efficient for people with depression. The study aims to compare the performance of three algorithms— Logistic Regression, Multinomial Naive Bayes and Support Vector Machine are the methods to detect depression. Any machine learning model’s accuracy is highly dependent on the quality of the training data. Collecting trustworthy and representative data may be difficult when attempting to forecast depression. The models’ performance may be impacted by the data’s potential for being incomplete, erroneous, or biased. A variety of measures, such as precision, recall analysis and F1-score comparison, will be utilized in order to evaluate the effectiveness of the algorithms. By training these models on a similar dataset and comparing them based on accuracy and other criteria like sensitivity rates, it can determine which algorithm performs better at predicting depression. The SVM tends to be more effective compared to other two algorithms.