DepressionSimple Android Application is a mobile application designed to help assess and predict users' mental health conditions such as Depression and Bipolar Disorder using an AI model. The application connects to a Python-based backend through an API and was developed as a university project.
- 🧠 AI-Powered Prediction – Predicts emotional conditions including Depression, Bipolar Type-1, and Type-2 using a trained ML model.
- 📝 Detailed Questionnaire – Users fill out a series of emotional and behavioral indicators.
- 📈 Clear Results – Returns a categorized result with an explanatory message.
- 🔗 API Integration – Connects the Kotlin mobile app to the backend through a secure POST request.
| Component | Description |
|---|---|
| 📱 Mobile Frontend (Kotlin) | Developed using Kotlin for Android to collect user input and display prediction results. |
| 🧠 AI Backend (Python Flask) | Provides REST API to serve predictions using a pre-trained Machine Learning model (Depression_AI.pkl). |
| 🔗 Communication | Frontend and backend communicate via HTTP POST requests using a form-based data structure. |
- Kotlin
- HTTP package for API communication
- Minimal, user-friendly UI design
- Python
- Flask
joblibto load the trained.pklmodel- Numpy
- RESTful API (deployed on port 3000)
The backend receives 17 symptom-related inputs from the user, including:
- Sadness
- Euphoric behavior
- Suicidal thoughts
- Trouble sleeping
- Aggression
- Overthinking
... and more.
The model then predicts the user's mental health state as follows:
| Prediction Code | Result |
|---|---|
| 0 | Normal |
| 1 | Bipolar Type-1 |
| 2 | Bipolar Type-2 |
| 3 | Depression |
| Other | Error |
| Parameter | Description |
|---|---|
| sadness | Level of sadness |
| euphoric | Euphoric behavior |
| exhausted | Fatigue |
| sleep | Sleep quality |
| swing | Mood swings |
| suicidal | Suicidal thoughts |
| anorxia | Appetite loss |
| authority | Authority conflict |
| explanation | Need for explanation |
| aggressive | Aggressiveness |
| move_on | Ability to move on |
| break_down | Breakdown tendency |
| admit | Acceptance |
| overthinking | Overthinking |
| sexual | Sexual behavior changes |
| concentration | Concentration ability |
| optimisim | Optimism level |
This project was developed as a part of a third-year university software project, aiming to apply real AI models into mobile applications. It demonstrates:
- Application of Machine Learning to real-world scenarios
- Mobile development using Kotlin
- Backend integration using Flask
- Psychological symptom analysis using AI