Neural Networks — Research Summary
A compact, visually-enhanced presentation of your model, architecture, and results.
Architecture: 3 hidden layers × 50 neuronsActivation: ReLU
Research summary
Neural networks are advanced computational models inspired by the structure and function of the human brain. They consist of layers of interconnected nodes, called neurons, which process information by passing signals from one layer to the next. The first layer, called the input layer, receives raw data such as demographic details or emotional states. This information flows through one or more hidden layers, where each neuron applies mathematical functions to identify complex patterns and relationships.
A key component in these hidden layers is the activation function, such as the Rectified Linear Unit (ReLU), which helps the network capture non-linear dependencies by activating only the most relevant signals and suppressing the rest. Finally, the output layer generates predictions or classifications based on the processed information.
Simple diagram: an input layer (age, gender, stress level), several hidden layers of neurons connected to each other (where computations happen), and an output layer producing results such as “low,” “moderate,” or “high” mood instability.
The neural network learns through training, adjusting connection strengths (weights) to minimize errors measured by a loss function. Through many epochs, the model improves its ability to detect subtle, non-linear patterns in data that traditional models might miss.
In this research, a neural network was employed to predict mood swings—an early and critical indicator of mental health challenges such as anxiety and depression—by analyzing a wide range of data. This included demographic variables (age, gender, employment), lifestyle factors (sleep, physical activity), and self-reported emotional states (stress, anxiety, depression). Data was sourced from a comprehensive mental health dataset, cleaned, encoded, and standardized to prepare it for analysis.
The model architecture consisted of three hidden layers, each containing 50 neurons activated by the ReLU function. This design enabled the network to capture complex interactions between input features and mood variability. Mood swings were classified into three categories: low, moderate, and high instability. The model was trained over 10 epochs using the Adam optimizer and categorical cross-entropy loss.
Results demonstrated an accuracy of 83%, with balanced precision, recall, and F1-scores across all classes. Sensitivity analysis revealed that a better mental health history significantly reduces the likelihood of mood swings, confirming the model’s meaningful representation of real-world patterns.
This approach underscores the potential for deep learning to enhance early diagnosis and personalized interventions in mental health care. By leveraging data-driven insights, neural networks can support clinicians and individuals alike, providing timely detection and tailored support to mitigate long-term psychiatric risks.
For the full research plan, detailed methodology, and complete results, visit: Full Paper Link
