Relationship between Clinicopathologic Variables in Breast Cancer Overall Survival Using Biogeography-Based Optimization Algorithm

Chuang, Li‐Yeh; Chen, Guangyu; Moi, Sin-Hua; Ouyang, Fusheng; Hou, Ming‐Feng; Yang, Cheng‐Hong

doi:10.1155/2019/2304128

Cited by 4 publications

(3 citation statements)

References 42 publications

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“…[4] used an alternative initial weight for adaptive penalized logistic regression to overcome the selection bias issue faced by the adaptive LASSO in a highdimensional cancer dataset. Finally, [24] used the PSO optimization algorithm for breast cancer diagnosis. [25] proposed a federated evolutionary feature selection algorithm that is based on PSO with low dimensional datasets to demonstrate that the proposed PSO-based algorithm has superior characteristics.…”

Section: Literature Reviewmentioning

confidence: 99%

Adaptive Elastic Net Based on Modified PSO for Variable Selection in Cox Model With High-Dimensional Data: A Comprehensive Simulation Study

Sancar,

Onakpojeruo,

Inan

et al. 2023

IEEE Access

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In contemporary research, high-dimensional data has become more popular in many scientific fields with the rapid advancement of technology in collecting and storing large datasets. As in any modeling process with high-dimensional data, it is very important to accurately identify a subset of the features and reduce the dimensionality in the Cox modeling process in the case of high-dimensionality. Numerous penalized techniques for the Cox model with high-dimensional data have been developed to handle the multicollinearity problem and decrease variability. Adaptive Elastic-net is one of the penalized methods used for feature selection that both handles the grouping effect and has the oracle property. However, providing these advantageous properties of Adaptive Elastic-net for variable selection in the Cox model depends on the optimal selection of hyperparameters, 𝛼, and 𝜆 values. For this reason, the appropriate selection of these parameters is quite important. Hyperparameters are generally selected by maximizing k-fold cross-validated log partial likelihood based on grid search over (𝛼, 𝜆) for the model. However, this method does not guarantee optimal α and λ values. In grid search, hyperparameters are typically allowed to take values specified in a limited sequence in a grid. The purpose of this study is to propose a novel method to determine the optimum hyperparameters (α, λ) pair of Adaptive Elastic-net for variable selection in the Cox model with high dimensional data based on modified particle swarm optimization (MPSO). The introduced metaheuristicbased method has been evaluated by extensive simulation studies by comparing it with different traditional penalized methods using various evaluation criteria under different scenarios. According to the comprehensive simulation study, the proposed method outperforms other penalized methods in terms of both variable selection and prediction and estimation accuracy performance for the Cox model in investigating the high-dimensional data.INDEX TERMS Adaptive elastic net, cox model, high-dimensional data, modified particle swarm optimization, variable selection.

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Section: Literature Reviewmentioning

confidence: 99%

Adaptive Elastic Net Based on Modified PSO for Variable Selection in Cox Model With High-Dimensional Data: A Comprehensive Simulation Study

Sancar,

Onakpojeruo,

Inan

et al. 2023

IEEE Access

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“…Furthermore, Santosa and Safitri [28] state that BBO is good at solving continuous problems. Applications of BBO to breast cancer include predicting breast cancer survival rates based on cancer's pathological features [29]. Zhang et al [27] recommended a hybrid BBO and FCM algorithm to overcome FCM's reliance on initial clusters.…”

Section: Biogeography-based Optimizationmentioning

confidence: 99%

Fuzzy C-Means Clustering: A Review of Applications in Breast Cancer Detection

Krasnov,

Davis,

Malott

et al. 2023

Entropy

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This paper reviews the potential use of fuzzy c-means clustering (FCM) and explores modifications to the distance function and centroid initialization methods to enhance image segmentation. The application of interest in the paper is the segmentation of breast tumours in mammograms. Breast cancer is the second leading cause of cancer deaths in Canadian women. Early detection reduces treatment costs and offers a favourable prognosis for patients. Classical methods, like mammograms, rely on radiologists to detect cancerous tumours, which introduces the potential for human error in cancer detection. Classical methods are labour-intensive, and, hence, expensive in terms of healthcare resources. Recent research supplements classical methods with automated mammogram analysis. The basic FCM method relies upon the Euclidean distance, which is not optimal for measuring non-spherical structures. To address these limitations, we review the implementation of a Mahalanobis-distance-based FCM (FCM-M). The three objectives of the paper are: (1) review FCM, FCM-M, and three centroid initialization algorithms in the literature, (2) illustrate the effectiveness of these algorithms in image segmentation, and (3) develop a Python package with the optimized algorithms to upload onto GitHub. Image analysis of the algorithms shows that using one of the three centroid initialization algorithms enhances the performance of FCM. FCM-M produced higher clustering accuracy and outlined the tumour structure better than basic FCM.

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“…Multiple machine learning approaches, including artificial neural network, 14 particle swarm optimization, 15 biogeography-based optimization, 16 and other hybrid methods have been widely used in the risk assessment of specific diseases. 17 Comparing with typical statistical approaches, the combination use of machine learning could overcome several limitations faced by statistical methods, including the sample size restriction and computation complexity.…”

Section: Introductionmentioning

confidence: 99%

Machine learning approaches for the mortality risk assessment of patients undergoing hemodialysis

Yang

Chen

Moi

et al. 2022

Therapeutic Advances in Chronic Disease

Self Cite

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Introduction: Mortality is a major primary endpoint for long-term hemodialysis (HD) patients. The clinical status of HD patients generally relies on longitudinal clinical observations such as monthly laboratory examinations and physical examinations. Methods: A total of 829 HD patients who met the inclusion criteria were analyzed. All patients were tracked from January 2009 to December 2013. Taken together, this study performed full-adjusted-Cox proportional hazards (CoxPH), stepwise-CoxPH, random survival forest (RSF)-CoxPH, and whale optimization algorithm (WOA)-CoxPH model for the all-cause mortality risk assessment in HD patients. The model performance between proposed selections of CoxPH models were evaluated using concordance index. Results: The WOA-CoxPH model obtained the highest concordance index compared with RSF-CoxPH and typical selection CoxPH model. The eight significant parameters obtained from the WOA-CoxPH model, including age, diabetes mellitus (DM), hemoglobin (Hb), albumin, creatinine (Cr), potassium (K), Kt/V, and cardiothoracic ratio, have also showed significant survival difference between low- and high-risk characteristics in single-factor analysis. By integrating the risk characteristics of each single factor, patients who obtained seven or more risk characteristics of eight selected parameters were dichotomized as high-risk subgroup, and remaining is considered as low-risk subgroup. The integrated low- and high-risk subgroup showed greater discrepancy compared with each single risk factor selected by WOA-CoxPH model. Conclusion: The study findings revealed WOA-CoxPH model could provide better risk assessment performance compared with RSF-CoxPH and typical selection CoxPH model in the HD patients. In summary, patients who had seven or more risk characteristics of eight selected parameters were at potentially increased risk of all-cause mortality in HD population.

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Relationship between Clinicopathologic Variables in Breast Cancer Overall Survival Using Biogeography-Based Optimization Algorithm

Cited by 4 publications

References 42 publications

Adaptive Elastic Net Based on Modified PSO for Variable Selection in Cox Model With High-Dimensional Data: A Comprehensive Simulation Study

Adaptive Elastic Net Based on Modified PSO for Variable Selection in Cox Model With High-Dimensional Data: A Comprehensive Simulation Study

Fuzzy C-Means Clustering: A Review of Applications in Breast Cancer Detection

Machine learning approaches for the mortality risk assessment of patients undergoing hemodialysis

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