Osteoporosis Risk Prediction

Overview

This project aims to detect bone fractures using machine learning and neural networks. It utilizes various machine learning models including AdaBoost, CatBoost, Logistic Regression, Random Forest, Support Vector Machine (SVM), XGBoost, Gradient Boosting, and LightGBM and and neural networks. The dataset used for this project is included along with the code.

Dataset

The dataset used in this project contains information about various factors that may contribute to osteoporosis risk, such as age, gender, hormonal changes, family history, race/ethnicity, body weight, calcium intake, vitamin D intake, physical activity, smoking, alcohol consumption, medical conditions, medications, and prior fractures.

Models Implemented

The following machine learning and neural network models are implemented in this project:

• AdaBoost
• CatBoost
• Logistic Regression
• Random Forest
• Support Vector Machine (SVM)
• XGBoost
• Gradient Boosting
• LightGBM

Preprocessing and Modeling

• Data preprocessing steps include handling missing values, encoding categorical variables, and standardizing numerical features.
• Various machine learning models are trained and evaluated, including logistic regression, random forest, AdaBoost, support vector machine (SVM), CatBoost, XGBoost, gradient boosting, and LightGBM.
• Advanced neural network architectures are also employed using TensorFlow/Keras, with different configurations of layers, activations, and regularization techniques.

Evaluation Metrics

• Evaluation metrics used for model performance assessment include accuracy, precision, recall, F1 score, and area under the ROC curve (AUC).
• Models are trained, validated, and tested to ensure robust performance and generalization to unseen data.

Hyperparameter Tuning

• Hyperparameter tuning is performed using techniques such as grid search and k-fold cross-validation to optimize model performance.
• Various combinations of hyperparameters are explored to find the best-performing model.

Analysis Process

1. Data Preprocessing: any preprocessing steps applied to the data, such as handling missing values, scaling features, encoding categorical variables, etc.

2. Model Training: Each model is trained using the training dataset and evaluated using the validation dataset. Hyperparameters are tuned as necessary.

3. Model Evaluation: After training, each model is evaluated using the testing dataset to assess its performance.

4. Results Visualization: Finally, the performance of all models is visualized using ROC curves to compare their performance in terms of accuracy, precision, recall, and F1-score.

Usage

To run the project, follow these steps:

1. Clone the repository.
2. Install the required dependencies (Python libraries).
3. Run the main script or Jupyter Notebook containing the code.
4. Analyze the results and evaluate the performance of each model.

Requirements

• Python 3.x
• Required Python libraries (list them if necessary)
• Jupyter Notebook (optional, if using a notebook)

Conclusion

In conclusion, this project demonstrates the effectiveness of machine learning and neural network models in bone fracture detection. Each model's performance can vary based on the dataset and hyperparameters. Further optimization and fine-tuning may improve overall performance.