Enhancing pancreatic cancer detection through RIDT-RLC: A robust ensemble approach with gradient descent logit boosting

The aggressive aggressiveness and sometimes silent beginning of pancreatic cancer make early identification a significant problem. In this research, we present RIDT-RLC, an innovative ensemble approach to pancreatic cancer diagnosis that combines a random indexive decision tree with a reinforcement learning classifier. In order to categorize the extracted data, the RIDT-RLC approach uses a number of basic classifiers that are row index decision trees. These classifiers are vital for calculating the similarity between the training and testing samples. Using similarity values for classification allows for the accurate detection of pancreatic cancer. A gradient descent logic boost classifier incorporates weak classifiers to enhance the classifier's performance even more. The diagnostic accuracy is much improved by using this ensemble technique, which produces a powerful and resilient classifier. Combining the capabilities of gradient descent logit boost classifiers with Rand Indexive Decision Trees, the RIDT-RLC technique shows promise for efficient pancreatic cancer diagnosis. The enhanced accuracy and reliability of the resulting classifier will help pave the way for more precise pancreatic cancer early detection tools and allow for faster medical intervention. The accuracy rate was 0.492 and the true-positive rate was 0.513 using the suggested method. The false-positive rate was 0.512. Furthermore, it achieved a specificity of 0.8, a precision of 0.37, and an accuracy of 0.79.