Lung-Cancer-Risk-Prediction-with-Machine-Learning-Models

This repository implements machine learning models for lung cancer risk prediction inspired by the paper:

Dritsas, E.; Trigka, M. (2022). Lung Cancer Risk Prediction with Machine Learning Models. Big Data and Cognitive Computing, 6(4), 139.
DOI: 10.3390/bdcc6040139

The paper demonstrates a comparative analysis of several classifiers (e.g., Naive Bayes, SVM, Random Forest, Rotation Forest, etc.) on a publicly available dataset and highlights the superior performance of the Rotation Forest classifier in terms of accuracy, precision, recall, F-Measure, and AUC.

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Overview

This project implements lung cancer risk prediction models using machine learning techniques. The key features of this repository include:

• Data Preprocessing: Balancing the dataset using SMOTE.
• Feature Analysis: Evaluating feature importance using methods like gain ratio and random forest.
• Modeling: Training a variety of classification models such as Naive Bayes, Bayesian network, logistic regression, SVM, Random Forest, and Rotation Forest.
• Evaluation: Assessing models with metrics including accuracy, precision, recall, F-Measure, and AUC via 10-fold cross-validation in the Weka environment.

The project is implemented in a Jupyter Notebook (ML_MINI_PROJECT.ipynb) that contains the code and experiments.

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Prerequisites and Installation

To run the code in this repository, please ensure you have the following:

• Python 3.x installed.
• Required Python libraries such as:
  • numpy
  • pandas
  • scikit-learn
  • imblearn (for SMOTE)
  • matplotlib or seaborn (for plotting)

You can install these dependencies using pip:

pip install numpy pandas scikit-learn imbalanced-learn matplotlib seaborn