THE APPLICATION OF MACHINE LEARNING IN EARLY DETECTION OF MENTAL HEALTH CONDITIONS THROUGH ANALYSIS OF HEALTHCARE CLAIMS DATA

Mental illness is a major world health issue, with increasing rates of depression, anxiety, and other psychiatric disorders affecting millions of people worldwide. Early diagnosis is crucial in preventing violent presentation and maximizing patient outcomes. However, conventional diagnosis largely relies on self-reports from patients and ratings by clinicians, thereby resulting in a delay in treatment. The present study takes into account the revolutionary potential of machine learning (ML) for early diagnosis of mental illness using healthcare claims data.
By analyzing large quantities of data, including demographic information, medical history, medication records, and usage patterns in healthcare, ML models can identify subtle patterns that indicate mental health risks. Supervised learning techniques such as logistic regression and deep neural networks achieve precise modelling of mental health status, whereas unsupervised learning techniques such as clustering uncover hidden at-risk groups. Current studies reveal that ML algorithms are capable of forecasting mental health diseases months before medical diagnosis, allowing for early intervention.
This paper strictly analyzes various ML techniques, their accuracy in the early detection of symptoms, and the challenges of employing ML in this area, such as data privacy, model explainability, and bias. Moreover, we discuss the contribution of XAI towards making ML-based predictions interpretable for health practitioners and policymakers. We also emphasize the importance of incorporating ML with electronic health records (EHRs) and real-time monitoring systems to improve predictive efficiency. It is indicated in the study that ML-based early detection frameworks can transform mental healthcare by changing the paradigm from reactive treatment to proactive intervention, thereby lessening the workload on healthcare systems and enhancing the quality of patient life.