Description:Field of Invention
The current invention pertains to the field of artificial intelligence (AI) and more particularly, the use of machine learning techniques for healthcare analytics. It facilitates the early detection and prediction of mental health disorders by synthesizing user behavior, self-reported symptoms, biometrics, and various other inputs. This invention is part of the interdisciplinary filed of computational psychiatry involving mental health studies, advanced data driven modeling, and intelligent prediction systems. The invention focuses on a scalable, non-intrusive, and efficient digital solution that identifies mental health risk, and provides timely interventions.
Objectives of the Invention
The primary purpose of this invention, called "Machine Learning Based User–Analyzed Prediction of Mental Health Disorders", is to create a system which can recognize and predict mental health conditions as early as possible using machine learning methods. The system aims to analyze user data such as self-reported symptoms, user behaviors, speech/text input, and physiological signals to identify risk of mental health disorders such as: depression, anxiety, stress, and pathologies, like: bipolar disorder. We will use structured and unstructured data sources in order to provide personalized mental health risk assessments and interventions as soon as possible.
Background of the Invention
Mental health disorders, such as depression, anxiety, bipolar disorder, and post-traumatic stress disorder (PTSD), impact millions of people around the globe and pose a significant threat to healthcare systems and society. Historically, mental health evaluations involve a considerable baseline of subjective clinical assessments, questionnaires, and in-person consultations (with the limited options of telehealth) and often leads to under-diagnosis, misdiagnosis, or late intervention. Due to social stigma, limited access to mental health specialists, and the often private characteristic of symptoms of mental illness, even those suffering from mental health issues often remain undiagnosed.
Several preceding inventions have exposed isolated techniques for monitoring emotions. For example, US Patent No. 10,840,321 describes the use of physiological data to monitor emotional changes but does not incorporate behavioral or linguistic data. US Patent No. 10,997,452 discusses predictive analytics in the field of healthcare, but it does so in the context of physical health conditions. US Patent No. 9,985,101 discusses emotion state identification through textual analysis, but does not possess the necessary depth to reliably predict clinical mental disorders. As established in German Patent DE102019201988A1, clear demand exists for intelligent systems capable of real-time, automated, and accurate behavioral detection, especially in high-stakes contexts. Likewise, with, and in relation to mental health, it is urgent to develop automated systems that move beyond emotion detection to provide predictive, clinically interpretable outputs that enable early intervention and ongoing mental health monitoring.
The present invention provides a solution to the limitations of the prior art through the implementation of a hybrid machine learning architecture, where text analytics (e.g., user journals, messages), biometric (e.g., heart rate, sleep patterns), and behavioral (intentional screen time specifying social levels) are monitored and analyzed to yield mental health risk scores. Moreover, advanced natural language processing (NLP) is used, including word embedding’s and sentiment analysis, with supervised machine learning (ML) models for classification and trend analysis (e.g., logistic regression, decision trees, deep learning networks).
Summary of the Invention
The invention provides a machine learning-based system for early detection and prediction of mental disorders. It gathers user responses from a structured survey and applies ensemble learning algorithms (Random Forest, Logistic Regression, SVM) for improved accuracy. A Super Learner Algorithm is used as a meta-model to further refine predictions. The final model is implemented on a cloud-based platform for real-time
Detailed Description of the Invention
The current invention discloses a real-time, intelligent system for predicting and tracking mental health disorders using machine learning methods. The invention addresses the increasing concern of undiagnosed and untreated mental health problems, especially a vulnerable population with little access to professional psychological care. The invention provides a simple, cloud integrated system that allows the user to self-report their mental state by filling in a structured psychological survey. The answers on that survey will be processed by an ensemble of machine learning models, which will then classify the likely presence of a mental disorder, along with a risk score, and possible recommendations, all in real-time.
The essential architecture of this invention is based on a three-phase user interaction. First, users enter into the system using a secured login interface on a mobile, web, or chatbot device. Second, users would be invited to complete a comprehensive mental health screening survey in order to identify cognitive, emotional, and behavioral characteristics. The survey is structured in a way that highlights recognized mental health conditions like depression, anxiety, bipolar disorder, schizophrenia, and post-traumatic stress disorder (PTSD) - each section provides empirically researched questions regarding symptom-specific characteristics - i.e. anxiety symptoms could include excessive worry and concentration issues, while depressive symptoms could include sleep problems and suicidal thoughts.
Data collected will be uploaded to a secure cloud server, where it will be subjected to a series of preprocessing steps, specifically: removing noise, normalization, feature scaling, and synthetic data generation. Each stage of preprocessing is necessary for the quality, uniformity, stability, and condition of the data to ensure representations are usable for machine learning purposes. Once cleaned and supplemented, the initial dataset of 50,000+ validated entries from clinical studies and public health sources, will be divided into three datasets: training, validation and testing. This will ensure continued accuracy of the model and allow the model to generalize across populations and demographic groups.
The invention relies on the Super Learner Algorithm, which uses ensemble learning methods to maximize prediction accuracy. The Super Learner algorithm is composed of a set of various base learners, each of which is Random Forest, Logistic Regression, and Support Vector Machines (SVM). Each model captures different dimensions of mental health data. Random Forest captures complex symptom dependencies, Logistic Regression provides probabilistic scores indicating a degree of endorsement for each disorder, and SVM captures momentary overlapping features of mental health. The output from the models provides directions and is fed into a meta-model, from which a consistent method learns the best way to combine predictions to improve overall classification accuracy and reduce errors. The service is being set up inside an already existing cloud infrastructure supporting real-time data handling and scalability. Users will receive immediate feedback upon completing the survey, indicating the likelihood that they suffer from specific mental disorders. The user interface will include a personalized dashboard showing risk levels for mental health issues, health recommendations, and options for engagement with professionals as necessary. Users rated at moderate to high risk of mental health issues will receive recommendations of evidence-based self-care strategies including mindfulness exercises, journal prompts, or lifestyle changes that can be enacted. Users and other care providers will always have the opportunity to connect with mental health professionals when needed, with the option for future recommend follow-up and intervention.
The invention will exist not only as a predictive service but also as a mechanism for tracking a user's long-term mental health. Users will have the ability to return to the service over time, re-take and comparison of tests as part of understanding changes in the user's mental condition. This ongoing feedback loop allows and enables users the opportunity for proactive control and ownership of their mental health status. At its core the combining of securely data managing user health and state, cloud-based processing, and smart model architecture means it was built from the ground up to be a scalable and plausible solution that can rapidly adopt and evolve both with the changes to technology and human behavior.

The system is designed to include data governance and privacy protection by obtaining user consent, anonymizing data, and encrypting both stored and in-transit data. Sensitive mental health data may be as sensitive or more sensitive than any other type of data, and the invention is designed to meet the requirements of relevant healthcare data regulations (e.g., HIPAA - Health Insurance Portability and Accountability Act; GDPR - General Data Protection Regulation). Users control their data inputs and are able to download, delete, or restrict access specifically to their historical survey responses. The system logs all data use and decision-making that users or the system engage in, and the resulting predictability is explainable, so transparency and explain ability of predictive models are supported—necessary attributes to establish user trust in AI-driven health tools.

In summary, this innovation is filling a crucial gap in mental health care by offering a scalable, accurate, and user-friendly platform for detecting mental health conditions and providing a management response. By utilizing ensemble machine learning models, the predictive abilities of the platform increase reliability. The cloud deployment and user interface design provides a platform that is accessible for multiple population groups. By identifying discreet, time specific, personalized, and evidence-based insights, this platform can truly revolutionize how mental health is approached during this emerging digital era.

Brief description of Drawing:
Figure 1: System Architecture of Proposed method , Claims:Claims:
A system for predicting mental disorders using ensemble learning techniques, comprising:
1 A structured survey-based data collection module, which includes

 a. A user interface for delivering standardized cognitive, emotional, and behavioral assessments.
 b. A categorized question bank based on various mental health disorders such as anxiety, depression, bipolar disorder, and PTSD.
 c. A response logging mechanism to capture and timestamp user input for analysis.

2. As mentioned in Claim 1, the system preprocesses data by: Removing inconsistencies such as spelling errors and abbreviation issues and encoded categorical variables into numerical formats for analysis.
3. As mentioned in Claim 1, the super Learner Algorithm integrates predictions from multiple base models to improve classification accuracy.
4. As mentioned in Claim 1, the frontend survey module allows users to enter responses, which are processed and analyzed in real-time.
5. As mentioned in Claim 1, the system is deployed on a cloud-based platform, ensuring scalability and accessibility for users.