COMPARATIVE ANALYSIS OF COLORECTAL CANCER BASED ON MACHINE LEARNING ALGORITHMS

Colorectal cancer (CRC) is the second leading cause of cancer-related deaths. Computational intelligence (CI)
has emerged as a promising tool to improve diagnosis, staging, and treatment, but evidence remains scattered
across the literature. This work aimed to predict Colorectal cancer patients using machine learning (ML)
methods. A retrospective analysis included in PubMed and EMBASE identified systematic reviews, following
PRISMA guidelines. Extracted data covered CI techniques, evaluation methods, target outcomes, and dataset
characteristics. Six ML methods, namely logistic regression (LR), Naïve Bayes (NB), Support Vector Machine
(SVM), Neural Network (NN), Decision Tree (DT), and Light Gradient Boosting Machine (LGBM), were
developed with 10-fold cross-validation. Feature selection employed the Random Forest method based on mean
GINI index criteria. which yields the highest accuracy (~96.2%) with better precision, recall, and F1-scores.
Time from diagnosis, age, tumor size, metastatic status, lymph node involvement, and treatment type emerged as
crucial predictors of Colorectal cancer based on mean GINI index. The NB models achieved the highest
predictive values for CRC patient. This study highlights the significance of variables including time from
diagnosis, age, tumor size, metastatic status, lymph node involvement, and treatment type in predicting CRC
patient. The NB model exhibited optimal efficacy in prediction, maintaining a balanced sensitivity and
specificity. Policy recommendations encompass early diagnosis and treatment initiation for CRC patients,
improved data collection through digital health records and standardized protocols, support for predictive
analytics integration in clinical decisions, and the inclusion of identified prognostic variables in treatment
guidelines to enhance patient outcomes.