论文标题

机器学习方法用于查找癌症的离散和预测治疗

Machine Learning Method Used to find Discrete and Predictive Treatment of Cancer

论文作者

Abtahi, SeyedMehdi, Sharifi, Mojtaba

论文摘要

癌症是全球最常见的疾病之一,对人类健康构成了严重威胁,并导致许多人死亡。在药物给药期间,在化学疗法中观察到免疫细胞,癌细胞和正常细胞被杀死或至少受到严重伤害,并且为了将药物的剂量保持在特定水平的体内,应在特定时间和剂量中递送药物。因此,为解决这些问题,需要一个决策过程来确定对癌症病例最合适的治疗方法,该治疗方法通过考虑将被杀死的健康细胞的数量来杀死癌细胞。尽管有最新的技术发展,但仍需要改进当前的方法,以证明最优化的肿瘤细胞药物剂量。预计我们提出的ANFIS模型能够建议专家最佳剂量的药物,该药物考虑了包括癌细胞,免疫和卫生细胞在内的所有关键因素。模拟的结果表现出在治疗过程中提出的智能控制器的高精度,以预测所有关键因素的行为,并最大程度地减少该药物的使用剂量,这一重要观点是,所提出的控制器提供了离散的治疗数据,可以填补工程和医学科学之间的空白。

Cancer is one of the most common diseases worldwide, posing a serious threat to human health and leading to the deaths of a large number of people. It was observed during the drug administration in chemotherapy that immune cells, cancer cells and normal cells are killed or at least seriously injured and also in order to keep dosage of the drug at specific level in body, drug should be delivered in specific time and dosage. Therefore, to address these problems, a decision-making process is needed to identify the most appropriate treatment for cancer cases which causes killing of cancer cells by considering the number of healthy cells that would be killed. Despite the latest technological developments, the current methods need to be improved to suggest the most optimized a dose of the drug for tumor cells discretely. It is expected that our proposed ANFIS model be able to suggest the specialists the most optimum dose of the drug, which considers all key factors including cancer cells, immune and health cells. The results of the simulations exhibit the high accuracy of the proposed intelligent controller during the treatment in predicting the behavior of all key factors and minimize the usage dose of the drug with regard this significant point that the proposed controller gives discrete data for treatment which can fill the gap between engineering and medical science.

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