Disease Prediction from Symptoms Using Machine Learning

Disease Prediction from Symptoms Using Machine Learning

I am excited to share one of my recent projects, a Disease Prediction System that uses machine learning to analyze symptoms and predict potential diseases. This project bridges the gap between technology and healthcare by providing users with a tool for faster and more efficient preliminary symptom analysis.

Project Highlights:

Symptom Analysis:

The system covers a wide range of symptoms (e.g., back pain, fever, yellowing of eyes) and predicts diseases such as Diabetes, Hypertension, Malaria, Typhoid, and more.

Machine Learning Models:

Implemented and compared three algorithms:

Decision Tree

Random Forest

Naive Bayes

Interactive User Interface:

Built using Python’s tkinter library.

Users input symptoms through dropdown menus, and predictions are displayed for all three models for better insights.

Accuracy and Evaluation:

Each model is rigorously trained and tested using metrics like accuracy, precision, recall, and F1-score to ensure reliability.

️ Technical Summary:

Tools & Libraries: Python, Scikit-learn, Pandas, NumPy, tkinter.

Training Data: Used labeled datasets for training and testing, mapping symptoms to diseases.

Model Output: Displays disease predictions based on user-input symptoms for all models.

This project demonstrates the power of AI in healthcare, offering a glimpse into how technology can assist in early diagnosis and empower individuals to seek medical attention with informed insights.