Disease Prediction Model
MediSense is an advanced predictive model designed to identify potential diseases based on user-provided symptoms. In addition to disease prediction, the application offers tailored health management solutions, including precautions, diet recommendations, medication suggestions, and workout plans.
- Accurate Disease Prediction: Predicts potential diseases with high accuracy using machine learning models trained on a robust dataset of 4,919 entries.
- Health Management Recommendations: Provides personalized precautions, dietary guidance, medication suggestions, and workout plans to support user health.
- Dual Model Accuracy:
- Achieved 96.72% accuracy with the MultinomialNB model.
- Achieved 83.60% accuracy with the GradientBoostingClassifier.
- Interactive User Experience: A seamless interface to input symptoms and receive actionable insights.
- Machine Learning Libraries:
- Pandas and NumPy: For data preprocessing and manipulation.
- Scikit-learn: For implementing and evaluating machine learning models.
- Models:
- Multinomial Naive Bayes (MultinomialNB)
- Gradient Boosting Classifier
The model is trained on a comprehensive dataset comprising 4,919 entries, which includes a wide range of symptoms and corresponding diseases. Data preprocessing was carried out to ensure high-quality training and evaluation.
- Siddhant Chatse - GitHub