Brain Sync

The system asks users to feed in their symptoms, processes the input using a machine learning model, and predicts a possible mental health condition. If necessary, it recommends the user consult a medical professional for further diagnosis and treatment.

1. Key Features:

1.1 Symptom-Based Assessment:

• The system asks users to report symptoms such as:

• Mood-related symptoms: Sadness, irritability, mood swings

• Cognitive symptoms: Difficulty concentrating, forgetfulness

• Physical symptoms: Fatigue, changes in sleep patterns, headaches

• Behavioral symptoms: Social withdrawal, loss of interest in activities

1.2 Machine Learning-based Diagnosis:

• Based on the symptoms provided, the system classifies the condition into possible mental health disorders such as:

• Depression

• Anxiety

• Bipolar Disorder

• Stress-related issues

• OCD tendencies

• The model is trained on publicly available and validated mental health datasets.

1.3 Doctor Recommendation System:

• If the predicted condition is severe or high-risk, the system suggests visiting a doctor for professional diagnosis.

• If the symptoms indicate mild to moderate distress, it may recommend self-care techniques such as meditation, lifestyle adjustments, or therapy.

2. Technical Implementation:

2.1 Data Collection:

• The training data is obtained from medical sources such as:

• PHQ-9 (Patient Health Questionnaire-9) for depression screening

• GAD-7 (Generalized Anxiety Disorder-7) for anxiety assessment

• Mental Health in Tech Dataset

• The dataset contains labeled mental health conditions based on symptom severity.

2.2 Preprocessing:

• Data cleaning and feature extraction are performed to improve model accuracy.

• Symptoms are converted into numerical form using:

• One-hot encoding (for categorical responses)

• TF-IDF (if using text-based input)

• Scaling techniques like Min-Max Scaling

2.3 Machine Learning Model:

• Model Choices:

• Decision Trees / Random Forest (for rule-based classification)

• Support Vector Machines (SVM) (for symptom classification)

• Neural Networks (if using deep learning for better pattern recognition)

• Training & Testing:

• The dataset is split into 80% training and 20% testing for model validation.

• The model is trained using Supervised Learning techniques.

• Performance is evaluated using metrics such as accuracy, precision, recall, and F1-score.

2.4 User Interface (UI):

• The front-end can be a simple web-based form or chatbot where users input their symptoms.

• Technologies used:

• Flask/Django for backend API

• HTML, CSS, JavaScript for the front-end

• REST API to connect the UI with the AI model

3. Workflow of BRAIN SYNC:

1. User submits symptoms through an online questionnaire.

2. The AI model processes the symptoms and matches them with existing patterns in the dataset.

3. The system outputs a predicted condition, such as anxiety, stress, or depression.

4. A recommendation is provided – either self-care advice or a suggestion to visit a doctor.

4. Future Improvements:

• More Advanced AI Models – Using deep learning (LSTMs or Transformers) for better predictions.

• Integration with Health Apps – Syncing with wearable devices to track stress levels.

• Speech & Voice Analysis – Analyzing tone and speech patterns for better mental health assessments.