School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, 632014, India.
Sci Rep. 2024 Nov 21;14(1):28880. doi: 10.1038/s41598-024-77302-z.
Artificial intelligence (AI) is an attractive field of Computer Science that helps to classify and to predict various real-time applications. Perhaps AI has a major role in predicting diseases at an early stage based on history. As cancer is one of the most harmful diseases where the mortality rate is high, it is now essential to utilize the benefits of AI to have an early diagnosis of cancer. Among various cancers, Colorectal cancer (CRC) is a common form of gastrointestinal cancer, and its treatment is lengthy and costly, with a high recurrence rate and high fatality rate. Initial disease analysis and prognosis are required to improve the patient's treatment with a better survival analysis. However, the disease prediction process depends on the collected data, where the data may contain uncertainty. Uncertain data leads to wrong predictions. Thus, it is essential to utilize rough computing, a mathematical tool to deal with uncertainty. This paper has made an effort, to handle uncertainty using a rough set of fuzzy approximation space as pre-processing and utilized Unidirectional and Bidirectional LSTM for the classification and prediction process. Thus, to demonstrate improved predictive accuracy, the proposed model adapted the optimizers and evaluated using benchmarking techniques in predicting stage-based survival rate. The comparative analysis shows that the proposed model performs well against the state-of-the-art models and can help the medical practitioner to detect CRC at an early stage and reduce the mortality rate among human beings.
人工智能（AI）是计算机科学中一个很有吸引力的领域，有助于对各种实时应用进行分类和预测。也许 AI 在基于历史数据的早期疾病预测方面具有重要作用。由于癌症是死亡率较高的最有害疾病之一，因此现在必须利用 AI 的优势进行早期癌症诊断。在各种癌症中，结直肠癌（CRC）是一种常见的胃肠道癌症，其治疗过程漫长且昂贵，复发率和死亡率都很高。需要进行初始疾病分析和预后，以改善患者的治疗效果和生存分析。然而，疾病预测过程取决于所收集的数据，而这些数据可能包含不确定性。不确定性数据会导致错误的预测。因此，利用粗糙集等数学工具来处理不确定性是很有必要的。本文努力使用模糊近似空间的粗糙集来处理不确定性，并在分类和预测过程中使用单向和双向长短时记忆网络（LSTM）。因此，为了证明改进的预测准确性，该模型采用了优化器，并使用基准测试技术在基于阶段的生存率预测方面进行了评估。对比分析表明，与最先进的模型相比，该模型表现良好，有助于医生在早期发现 CRC，并降低人类的死亡率。
