A comprehensive evaluation of data analysis approaches for predicting colorectal cancer.

Early diagnosis of colorectal cancer (CRC), a global health issue, improves patient outcomes. This comprehensive study assesses data analysis strategies to predict CRC and enhance diagnosis and prognosis. Data preparation, feature selection (FS), and machine learning (ML) techniques help us develop reliable and timely cancer diagnostic procedures. Analysis of data is crucial for early cancer detection. As more patient data is collected, machine learning can find patterns and associations that traditional diagnostic approaches cannot. This study uses these sources to reveal that CRC prediction requires a negative approximation. We did a rigorous preliminary data analysis using medical records, genetic markers, and demographic data to address ineffectiveness and negativity. Decide the best CRC prediction parameters using FS. Machine learning algorithms, including decision tree (DT), logistic regression (LR), neural network (NN), and deep learning (DL) like CNN and RNN, are employed. The dataset capacity determines LSTM and GAN adaptation. We evaluate the accuracy, precision, recall, and F1 score to understand the model's performance. We found the pros and cons of each literature review strategy. Some models perform better in particular situations, while others are more accurate and precise. The importance of FS in model performance emphasizes the need to consider differences. The findings help researchers and physicians improve CRC detection and patient outcomes. [ABSTRACT FROM AUTHOR]