MACHINE LEARNING-BASED BIOMARKER EARLY DETECTION OF MENTAL HEALTH DISORDERS

Abstract

Mental disorders have become one of the key public health problems, affecting people in all sorts of demographics and of all ages. Diagnoses for depression, anxiety, post-traumatic stress disorder (PTSD), and bipolar disorder depend heavily on self-reported symptoms, clinical examination, and behavioral observations, making the problem less evident and thus delaying the treatment process. This in turn may lead to a series of consequences related to the patients' psychological state. Hence, there is an increased need for smart systems that can help health professionals detect the presence of the disease at an early stage. 3 This paper suggests a new machine-learning algorithm for early detection of mental disorders through biomarkers. The main goal of the algorithm is to analyze various biological indicators (heart rate variability, sleep activity, emotion signals, behavior patterns, and digital behavior indicators) in order to diagnose the diseases based on their corresponding symptoms. The following machine learning and deep learning methods were used in the analysis: logistic regression, support vector machines (SVM), random forest (RF), XGBoost algorithm, and long short-term memory (LSTM). This paper provides detailed information about data preparation, classification of mental disorders, and comparison of the above-listed machine learning algorithms in terms of their accuracy and performance. For experiments, the authors have used various health and mental health datasets obtained from publicly available resources. In empirical results, the authors conclude that ensemble and deep learning algorithms perform better than classical methods. In conclusion, the use of artificial intelligence in healthcare allows us to make an important step forward in terms of detecting diseases at an early stage, as well as providing opportunities for further diagnosis and analysis using biomarkers. It helps us identify various health disorders before we can actually start noticing symptoms. Additionally, this paper highlights the importance of data privacy when dealing with health information, explains how to solve problems caused by mismatched datasets and lack of explainability, and describes how to implement the system in situ. Future improvements might focus on integrating wearables into health applications, working on explainable AI techniques, analyzing multimodal health-care data, and designing intelligent cloud health care systems.