A Dynamic Model for Imputing Missing Medical Data: A Multiobjective Particle Swarm Optimization Algorithm

Abstract

Missing data occurs in all research, especially in medical studies. Missing data is the situation in which a part of research data has not been reported. This will result in the incompatibility of the sample and the population and misguided conclusions. Missing data is usual in research, and the extent of it will determine how misinterpreted the conclusions will be. All methods of parameter estimation and prediction models are based on the assumption that the data are complete. Extensive missing data will result in false predictions and increased bias. In the present study, a novel method has been proposed for the imputation of medical missing data. The method determines what algorithm is suitable for the imputation of missing data. To do so, a multiobjective particle swarm optimization algorithm was used. 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.

Missing Value Imputation Designs and Methods of Nature-Inspired Metaheuristic Techniques: A Systematic Review

“…The advantages of this approach are that it is robust and able to handle online imputation and classification simultaneously for MAR missingness type. The proposed multi-objective particle swarm optimization (MOPSO) approach in [23] determined the optimal imputation algorithm based on the MCAR, MAR, and MNAR missingness mechanisms, in which the fitness function adapted according to sensitivity and specificity. The proposed MOPSO improved the imputation accuracy by 16.52% than the delete missing, mean, expectation-maximization, multivariate imputation by chained equations (MICE), and missForest imputation approaches.…”

“…Multi objective approaches Hybrid approaches GA [16]- [18] GP [19] PSO [20] MOGA-II [21]- [22] MOPSO [23] Bayesian ACO+ Bayesian [24] ABC+ Bayesian [25] Max-min ACO +bayesian [26]- [27] Bayesian+ tensor+chaotic PSO [28] Probabilistic GA+KNN [29] GMSA+MPSO+ WKNN [30] PSO+ covariance matrix [32] IDW+TR+ PSO [33] Clustering ACO+ clustering [34] FCM+GA [35][36] FCM+ SVR+GA [37] GA+SOM [38] FCM+PSO [39]- [42] GFM+PSO [43] PSO-ECM+ AAELM [44] ELM+PSO+ FCM [45] PSO+K-means+ ontology [46] SOM+FOA +LSSVM [47] DE+ clustering [48] GA+RF [49] GP+wrapper [55] [56] Neural network GSO+MLP [57] GA+MLP, SA+MLP, PSO+MLP, RF+MLP [58] SC-FITNET [59] SC-FDO+ MLP [60] DL-CS [61] DL-BAT [62] DL-GSA [63] PSO+LSVM [54] PSO+levy flight+SVM [53] MAIS+GA [50] GA+ARO [51] GP+tree vector [52] KNN+LAHC AWOA [31] The proposed approach enhanced imputation for missing multivariate data…”

“…Other than that, accuracy is used to measure the performance of the imputation method with respect to classifier accuracy [17], [23], [27], [28], [40], [41], [48], [50]. The MOGA-II imputer [21] achieved an accuracy of 82.9%, outperforming the GA imputer [18] and GA+ARO imputer [51] when handling missing values for the pima Indian dataset.…”

Corrigendum to “A Dynamic Model for Imputing Missing Medical Data: A Multiobjective Particle Swarm Optimization Algorithm”

“…In the article titled “A Dynamic Model for Imputing Missing Medical Data: A Multiobjective Particle Swarm Optimization Algorithm” [ 1 ], there was a spelling error in affiliation two. The correct affiliation is “Shahroud University of Medical Sciences, Shahroud, Iran” and it is shown above.…”

“…In the article titled "A Dynamic Model for Imputing Missing Medical Data: A Multiobjective Particle Swarm Optimization Algorithm" [1], there was a spelling error in a liation two.…”