Optimization of Gradient Descent and Loss Function: Application to Prediction of Depression by Multidimensional Analysis of Key Factors

Abstract

This study explores the application of gradient descent optimization and loss functions for depression prediction through multidimensional analysis of behavioral, psychological, and social factors. A logistic regression model was trained on a set of 1,631 records. Experimental results indicate an overall accuracy of 91.17 %, a weighted precision of 0.92, a recall of 0.91, and an F1 score of 0.91. These performances demonstrate the robustness of the model and its potential for early detection of depression. This research highlights the role of artificial intelligence in decision support and prevention in mental health. This article explores in depth the gradient descent algorithm, a fundamental pillar of machine learning, focusing on its essential interaction with the loss function (or cost function). Far from a simple theoretical analysis, this talk applies these fundamental concepts to a crucial public health problem: the prediction of depression. Depression is a complex disorder, and its diagnosis and early prediction remain major challenges. To develop reliable predictive models of depression, it is imperative to identify and integrate a diverse set of relevant factors. We will rely on a set of 24 input variables, grouped into 8 symptomatic and contextual categories (such as concentration difficulties, feelings of guilt, sleep disturbances, intense fatigue, etc.), to build our predictive model. The challenge is to navigate the space of these multidimensional parameters in order to find the optimal combination minimizing the prediction error of depression. This paper details how gradient descent, guided by a carefully chosen loss function, addresses this prediction challenge: Modeling of input variables, Application and optimization of gradient descent and Convergence and performance analysis. By integrating these specific variables into a rigorous gradient descent optimization approach, this paper will seek to demonstrate the feasibility and effectiveness of a machine learning model for more accurate and meaningful prediction of depression.