A hybrid machine learning model combining association rule mining and classification algorithms to predict
Feyza Firat Atay1, Fatma Hilal Yagin2, Cemil Colak2
1Department of Internal Medicine and Medical Oncology, Faculty of Medicine, Inonu University, Malatya, Turkey.
Frontiers in Medicine
|October 21, 2024
Summary
A new model accurately predicts differentiated thyroid cancer recurrence using clinical and pathological data. This helps personalize patient management and improve outcomes for this common endocrine malignancy.
Area of Science:
- Endocrinology
- Oncology
- Data Science
Background:
- Differentiated thyroid cancer (DTC) is a common endocrine malignancy with a significant recurrence rate.
- Current predictive methods for DTC recurrence require enhancement for effective patient management.
Purpose of the Study:
- To develop a relational classification model for predicting DTC recurrence.
- To integrate clinical, pathological, and follow-up data for improved prediction accuracy.
Main Methods:
- Utilized a dataset of 550 DTC samples with 13 clinicopathological variables.
- Employed Synthetic Minority Over-sampling Technique for Nominal and Continuous (SMOTE-NC) to address class imbalance.
- Developed a hybrid model combining classification algorithms and association rule mining, including Regularized Class Association Rules (RCAR) and Classification Based on Association Rules (CBAR).
Main Results:
- The RCAR model outperformed CBAR, achieving 96.7% accuracy, 93.1% sensitivity, and 96.7% F1 score.
- Papillary pathology with incomplete response and lymphadenopathy were identified as strong predictors of recurrence.
- Absence of adenopathy and complete treatment response correlated with no recurrence.
Conclusions:
- Introduced a robust and interpretable predictive model for personalized thyroid cancer care.
- The model facilitates identification of high-risk patients for tailored follow-up strategies.
- Potential to improve patient outcomes and optimize resource allocation in DTC management.
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