This project aims to predict the likelihood of diseases based on user inputs, utilizing machine learning models and exploratory data analysis (EDA). The system takes various health parameters and outputs a prediction for potential diseases. It helps users take preventive actions and seek timely medical attention.
- Data sourced from healthcare datasets containing information on various health parameters and disease history.
- Includes features such as age, weight, blood pressure, cholesterol, and more, depending on the type of disease being predicted.
- Handled missing values and outliers.
- Standardized the input features for better model performance.
- Encoded categorical variables (e.g., gender, smoking history, etc.).
- Created additional derived features based on existing data, such as Body Mass Index (BMI) from weight and height.
- Handled feature scaling using techniques like normalization or standardization for model accuracy.
- Distribution plots to visualize the spread of numerical features like age, cholesterol, etc.
- Correlation heatmap to identify relationships between features and the target disease.
- Boxplots to check for outliers in important health indicators.
- Applied classification algorithms like Logistic Regression, Decision Trees, Random Forests, or Support Vector Machines (SVM) for disease prediction.
- Model evaluation using metrics like accuracy, precision, recall, and F1-score.
- Programming Language: Python
- Data Analysis: Pandas, NumPy
- Data Visualization: Matplotlib, Seaborn
- Machine Learning: Scikit-learn, XGBoost
- Web App Deployment: Streamlit (for real-time predictions)
- Health parameters like age, cholesterol, and smoking history are significant factors influencing disease prediction.
- Log transformations helped normalize skewed features such as BMI and cholesterol.
- The model can accurately predict the likelihood of diseases based on key health indicators, assisting in early detection and prevention.
The analysis and model include:
- Histograms and Boxplots: To explore the distribution and outliers of health indicators.
- Bar Charts: For categorical feature comparisons (e.g., gender, smoking history).
- Correlation Heatmap: To understand the relationships between health parameters and the predicted disease.
- Clone the repository: git clone https://github.com/yourusername/disease-prediction.git
2.Ensure the required files are in place: dataset.csv: The health dataset used for model training. model.pkl: The trained machine learning model for predictions.
3.Install dependencies: pip install pandas numpy matplotlib seaborn scikit-learn streamlit
4.Run the Streamlit application: streamlit run app.py
User Input: Input health parameters such as age, blood pressure, cholesterol levels, BMI, etc.
Prediction: The model predicts the likelihood of specific diseases (e.g., diabetes, heart disease, etc.) based on the input parameters.
Output: A probability score indicating the likelihood of the user contracting a disease, along with relevant recommendations.
Integrate additional datasets for a broader range of diseases.
Implement deep learning models for improved prediction accuracy.
Expand the web interface with more user-friendly features like visual explanations of the predictions.
Deploy the model as a REST API for integration with healthcare applications.
The dataset used in this project is from publicly available healthcare datasets (e.g., Diabetes, Heart Disease datasets). The dataset contains health-related attributes and disease outcomes.