AI-Driven Detection of Depressive Disorders: A Machine Learning Approach (Trinity Journal of Management, IT & Media (TJMITM), 2025)
This research centres on the identification of patients experiencing depression. To identify the condition, this paper suggests three machine learning methodologies: Artificial Neural Network (ANN), Support Vector Machine (SVM), and Multi Linear Regression (MLR) models. The primary aim was pinpointing the most efficient model for accurately recognising depression. Following a thorough evaluation, the ANN model displayed superior performance among the trio, achieving 100% accuracy in depression identification. The ANN model’s architecture, comprising one input, hidden, and output layer, proved to be a better fit for the data, resulting in fewer errors than the SVM and MLR models. The study utilised accuracy and loss graphs from training and validation datasets and a confusion matrix to evaluate model efficacy. The results confirm that the ANN model surpasses the others, particularly regarding cross-entropy loss. These findings highlight the ANN model’s potential usefulness in sending timely text messages or alerts to healthcare professionals based on the patient’s condition and treatment needs.
Keywords: ANN; SVM; MLR; Depression; healthcare