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Background <p>If metastasis can be predicted, appropriate management of lung cancer will be possible to improve prognosis. Therefore, the main objective of this study is to develop a prediction model with appropriate performance for predicting metastasis based on patient data.</p> Methods <p>The study analyzed patient records from Kaggle, performing exploratory data analysis and preprocessing techniques such as missing values handling, encoding categorical variables, and outliers’ removal. Next, Data normalization and feature engineering were applied. Imbalanced data was addressed using Synthetic Minority Over-sampling Technique (SMOTE). Supervised classification models, including boosting algorithms were developed and tuned via Grid Search algorithms. Performance was evaluated using accuracy, precision, recall, F1-score, and ROC-AUC, with stratified k-fold cross-validation ensuring robustness. All processes were conducted using Python language on Google Collab.</p> Results <p>After data preprocessing, normalization, and selecting more related features, the dataset was stratified into training and testing subsets using an 80:20 split to ensure representative sampling classification models trained on training data. Class imbalance in the target variable was addressed using the SMOTE on the training set to balance classes without biasing the test set. Various machine learning models, including Logistic Regression, KNN, Decision Tree, Random Forest, and boosting algorithms (AdaBoost, XGBoost, LightGBM, Gradient Boosting), were developed and evaluated using metrics like accuracy, precision, recall, F1-score, and AUC. SMOTE significantly improved model performance, with XGBoost emerging as the top performer, achieving the highest AUC (82%) and F1-score (74.83%). ROC curve analysis confirmed XGBoost’s superior predictive power for metastasis.</p> Conclusions <p>The development of a high-performing prediction model for lung cancer metastasis to other organs has significant implications for clinical practice. Early and accurate prediction of metastasis can enable timely interventions, potentially improve patient outcomes and reduce healthcare costs.</p>

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Developing a prediction model to predict the incidence of metastasis in lung cancer patients using artificial intelligence techniques

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月27日

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  • Mehrnaz Asadi Gharabaghi,
  • Marsa Gholamzadeh

摘要

Background

If metastasis can be predicted, appropriate management of lung cancer will be possible to improve prognosis. Therefore, the main objective of this study is to develop a prediction model with appropriate performance for predicting metastasis based on patient data.

Methods

The study analyzed patient records from Kaggle, performing exploratory data analysis and preprocessing techniques such as missing values handling, encoding categorical variables, and outliers’ removal. Next, Data normalization and feature engineering were applied. Imbalanced data was addressed using Synthetic Minority Over-sampling Technique (SMOTE). Supervised classification models, including boosting algorithms were developed and tuned via Grid Search algorithms. Performance was evaluated using accuracy, precision, recall, F1-score, and ROC-AUC, with stratified k-fold cross-validation ensuring robustness. All processes were conducted using Python language on Google Collab.

Results

After data preprocessing, normalization, and selecting more related features, the dataset was stratified into training and testing subsets using an 80:20 split to ensure representative sampling classification models trained on training data. Class imbalance in the target variable was addressed using the SMOTE on the training set to balance classes without biasing the test set. Various machine learning models, including Logistic Regression, KNN, Decision Tree, Random Forest, and boosting algorithms (AdaBoost, XGBoost, LightGBM, Gradient Boosting), were developed and evaluated using metrics like accuracy, precision, recall, F1-score, and AUC. SMOTE significantly improved model performance, with XGBoost emerging as the top performer, achieving the highest AUC (82%) and F1-score (74.83%). ROC curve analysis confirmed XGBoost’s superior predictive power for metastasis.

Conclusions

The development of a high-performing prediction model for lung cancer metastasis to other organs has significant implications for clinical practice. Early and accurate prediction of metastasis can enable timely interventions, potentially improve patient outcomes and reduce healthcare costs.