TY - Jour A2 - Hasikin，Khairunnisa Au - Almasinejad，Peyman Au - Golabpour，Amin Au - Mollakhalili Meybodi，Mohammad Reza Au - Mirzaie，Kamal Au - Khosravi，Ahmad Py - 2021Da - 2021/10/08 Ti - 一种动态模型为了抵消缺失的医疗数据：多目标粒子群优化算法SP - 1203726 VL - 2021 AB - 缺失数据在所有研究中发生，特别是在医学研究中。缺少数据是尚未报告研究数据的一部分的情况。这将导致样本和人口的不相容性和误导的结论。缺少数据通常在研究中，它的范围将决定如何误解结论。参数估计和预测模型的所有方法都基于数据完成的假设。广泛的缺失数据将导致虚假预测和增加的偏差。在本研究中，已经提出了一种新的方法，用于医疗缺失数据的归档。该方法确定什么算法适用于缺失数据的归属。为此，使用多目标粒子群优化算法。 The algorithm imputes the missing data in a way that if a prediction model is applied to the data, both specificity and sensitivity will be optimized. Our proposed model was evaluated using real data of gastric cancer and acute T-cell leukemia (ATLL). First, the model was then used to impute the missing data. Then, the missing data were imputed using deletion, average, expectation maximization, MICE, and missForest methods. Finally, the prediction model was applied for both imputed datasets. The accuracy of the prediction model for the first and the second imputation methods was 0.5 and 16.5, respectively. The novel imputation method was more accurate than similar algorithms like expectation maximization and MICE. SN - 2040-2295 UR - https://doi.org/10.1155/2021/1203726 DO - 10.1155/2021/1203726 JF - Journal of Healthcare Engineering PB - Hindawi KW - ER -