Enhancing Cancer Prediction Accuracy Through Real-Time Monitoring and Artificial Intelligence Analysis for Patients

Abstract

The integration of Artificial Intelligence (AI) into clinical medicine has recently garnered significant attention, particularly in the context of digital pathology and precision medicine. Real-time monitoring and analysis of patients play a pivotal role in the overall treatment process. This includes monitoring basic body detection indices, analyzing the patient's overall condition, and predicting future possibilities for their medical status. In addition to fundamental machine learning (ML), both deep learning (DL) and digital twin technology have demonstrated considerable potential in enhancing the accuracy of analysis and prediction in clinical cancer patients' condition monitoring. This study employs a comparative analysis approach to assess and compare how DL and digital twin technology contribute to improving accuracy in monitoring and analyzing clinical cancer patients' conditions. ML is well-suited for training models with large datasets, while DL excels in tackling more intricate problems. To enhance the precision and accuracy of analysis and future prediction, digital twin technology is leveraged to explore various patient scenarios, with basic twins providing real-time monitoring of the patient's current conditions. Markov Decision Progress (MDP) is employed when predicting potential outcomes. However, it's essential to consider the impact of the number of times the model is trained when making comparisons. The evaluation criterion centers on whether the use of DL and digital twin technology improves accuracy and precision compared to using ML alone. Examining the results reveals that using only ML or DL yields accuracies of 97.01% and 94.73%, respectively. However, when ML is combined with DL and digital twin technology, accuracy significantly improves to 99.73%.