Missing data imputation using statistical and machine learning methods in a real breast cancer problem

Jerez, José M.; Molina, Ignacio; García-Laencina, Pedro J.; Alba, Emilio; Ribelles, Nuria; Martín, Miguel; Franco, Leonardo

doi:10.1016/j.artmed.2010.05.002

Cited by 436 publications

(241 citation statements)

References 40 publications

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“…Robustness, another performance measure, evaluates the ability of the model to make correct predictions, given the noisy data or data with missing values. 17,27,46 ML algorithms handle missing values in different ways that may improve predictive accuracy in comparison to standard techniques. 46 Deployment: Creation of the model is generally not the end of the project and refining the results, making them practical for end users (that is, clinicians), is necessary.…”

Section: Training Setmentioning

confidence: 99%

“…17,27,46 ML algorithms handle missing values in different ways that may improve predictive accuracy in comparison to standard techniques. 46 Deployment: Creation of the model is generally not the end of the project and refining the results, making them practical for end users (that is, clinicians), is necessary. Optimally, the models serve as the basis for the generation of decision-support systems.…”

Section: Training Setmentioning

confidence: 99%

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Application of machine learning algorithms for clinical predictive modeling: a data-mining approach in SCT

Shouval¹,

Bondi

Mishan

et al. 2013

Bone Marrow Transplant

102

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Data collected from hematopoietic SCT (HSCT) centers are becoming more abundant and complex owing to the formation of organized registries and incorporation of biological data. Typically, conventional statistical methods are used for the development of outcome prediction models and risk scores. However, these analyses carry inherent properties limiting their ability to cope with large data sets with multiple variables and samples. Machine learning (ML), a field stemming from artificial intelligence, is part of a wider approach for data analysis termed data mining (DM). It enables prediction in complex data scenarios, familiar to practitioners and researchers. Technological and commercial applications are all around us, gradually entering clinical research. In the following review, we would like to expose hematologists and stem cell transplanters to the concepts, clinical applications, strengths and limitations of such methods and discuss current research in HSCT. The aim of this review is to encourage utilization of the ML and DM techniques in the field of HSCT, including prediction of transplantation outcome and donor selection.

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Section: Training Setmentioning

confidence: 99%

Section: Training Setmentioning

confidence: 99%

Application of machine learning algorithms for clinical predictive modeling: a data-mining approach in SCT

Shouval¹,

Bondi

Mishan

et al. 2013

Bone Marrow Transplant

102

View full text Add to dashboard Cite

show abstract

“…This is mainly the result of their capability to capture complex, nonlinear and dynamic relationships in function generalization and regression as well as classification of data. Specifically, evolutionary algorithms, including genetic programming, feed forward back propagation neural networks, support vector machines, and deep learning algorithms [12,31,32] are effective at recognizing subtle patterns and thus have been employed to characterize the complex relationships between the cloudy and cloud-free pixels in the historical time series over spatial and spectral domains [15]. However, clustered missing data such as seasonal storm clouds over multiyear time scales make the compilation of viable training and test data difficult.…”

Section: Data Recovery Using Machine Learning Methodsmentioning

confidence: 99%

“…The importance of data recovery in numerous fields such as medical [15,16], neuro-computation [17], and climate science [18,19] has long been realized. Several approaches have been applied to recover the value of missing data.…”

Section: Past Research On Data Recoverymentioning

confidence: 99%

Optical Cloud Pixel Recovery via Machine Learning

et al. 2017

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Remote sensing derived Normalized Difference Vegetation Index (NDVI) is a widely used index to monitor vegetation and land use change. NDVI can be retrieved from publicly available data repositories of optical sensors such as Landsat, Moderate Resolution Imaging Spectro-radiometer (MODIS) and several commercial satellites. Studies that are heavily dependent on optical sensors are subject to data loss due to cloud coverage. Specifically, cloud contamination is a hindrance to long-term environmental assessment when using information from satellite imagery retrieved from visible and infrared spectral ranges. Landsat has an ongoing high-resolution NDVI record starting from 1984. Unfortunately, this long time series NDVI data suffers from the cloud contamination issue. Though both simple and complex computational methods for data interpolation have been applied to recover cloudy data, all the techniques have limitations. In this paper, a novel Optical Cloud Pixel Recovery (OCPR) method is proposed to repair cloudy pixels from the time-space-spectrum continuum using a Random Forest (RF) trained and tested with multi-parameter hydrologic data. The RF-based OCPR model is compared with a linear regression model to demonstrate the capability of OCPR. A case study in Apalachicola Bay is presented to evaluate the performance of OCPR to repair cloudy NDVI reflectance. The RF-based OCPR method achieves a root mean squared error of 0.016 between predicted and observed NDVI reflectance values. The linear regression model achieves a root mean squared error of 0.126. Our findings suggest that the RF-based OCPR method is effective to repair cloudy pixels and provides continuous and quantitatively reliable imagery for long-term environmental analysis.

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“…• The second challenge in data mining is the missing data imputation technique itself, as the presence of MVs affects the data quality, generate bias and so on [18,21].…”

Section: Challengesmentioning

confidence: 99%

Hybrid Kernel Fuzzy Clustering with Feed Lion Neural Network for Missing Data Imputation and Classification

Al¹

2017

IJCSEITR

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A common issue in many practical applications associated with pattern classification is data incomplete or missing data due to various reasons that differ based on the applications. Missing data imputation is a promising approach used to handle this issue, which fills the missing attributes with estimated values following several techniques. This paper proposes a new strategy for data imputation and classification using a hybrid prediction model that combines Hybrid Kernel Fuzzy Clustering (HKFC) and Feed Lion Neural Network (FLNN). FLNN considers the missingattributes as the class attributes to predict the missing data, which will also be computed using HKFC and finally, the combined effect of these two approaches provides the missing data. FLNN is designed for the classification by modifying

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Missing data imputation using statistical and machine learning methods in a real breast cancer problem

Cited by 436 publications

References 40 publications

Application of machine learning algorithms for clinical predictive modeling: a data-mining approach in SCT

Application of machine learning algorithms for clinical predictive modeling: a data-mining approach in SCT

Optical Cloud Pixel Recovery via Machine Learning

Hybrid Kernel Fuzzy Clustering with Feed Lion Neural Network for Missing Data Imputation and Classification

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