Predicting the Risk of Depression Based on the Patient's Chronic Diseases and Other Physiological Attributes

Abstract

The prevalence of chronic diseases like diabetes, hypertension, and chronic respiratory conditions contributes significantly to global mortality rates. In addition, there is growing evidence that links mental health disorders to physical activity, body mass index (BMI), and chronic diseases, highlighting their importance in public health research. This study investigates the intricate relationship between chronic diseases and depression, employing innovative Machine Learning techniques to predict depression likelihood based on various health comorbidities. Results indicate that Naive Bayes consistently outperforms other models, highlighting its potential for accurate predictions. The trade-off between specificity and accuracy, however, highlights the necessity of balanced datasets in real-world applications.