Developing a lung cancer detection system in R involves data collection, preprocessing, feature selection, model development with machine learning algorithms like Logistic Regression SVM and Random Forest, and evaluation for deployment in healthcare settings.
"Lung Cancer Detection System using R" This R script is designed to develop a lung cancer detection system using machine learning techniques. It utilizes various algorithms such as Support Vector Machine (SVM), Random Forest, and Logistic Regression for classification.
Instructions:- Install Required Libraries: Ensure you have installed the necessary R libraries mentioned in the script by running install.packages(c("caret", "e1071", "ggplot2", "ROCR", "pROC", "dplyr", "randomForest", "glmnet")). Load Dataset: Load your lung cancer dataset using read.csv(file.choose()). Ensure the dataset contains relevant attributes including patient demographics, clinical history, and imaging features. Data Preprocessing: The script handles preprocessing tasks such as converting column names to lowercase, converting categorical variables to factors, and removing rows with missing values. Exploratory Data Analysis (EDA): Explore the dataset using summary statistics, histograms, bar plots, scatter plots, and box plots provided in the script. Model Training: Train classification models including SVM, Random Forest, and Logistic Regression using the preprocessed dataset. Model Evaluation: Evaluate the trained models using confusion matrices and performance metrics such as accuracy, precision, recall, sensitivity, and F1-score. Comparison: Compare the accuracy of different algorithms using a bar graph.
Usage:- Clone this repository. Run the R script in an R environment or RStudio. Modify the script as needed for your specific dataset and requirements. Experiment with different algorithms and parameters to improve model performance. Document your findings and results accordingly.
Note:- Ensure your dataset is appropriately formatted and contains relevant features for lung cancer detection. Customize the script to suit your specific dataset and analysis requirements. Refer to the comments in the script for detailed explanations of each step and function.
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