Competitive evaluation of data mining algorithms for use in classification of leukocyte subtypes with Raman microspectroscopy

The Analyst ◽  
2015 ◽  
Vol 140 (7) ◽  
pp. 2473-2481 ◽  
Author(s):  
A. Maguire ◽  
I. Vega-Carrascal ◽  
J. Bryant ◽  
L. White ◽  
O. Howe ◽  
...  
Keyword(s):  
Data Mining ◽  
Mononuclear Cells ◽  
Peripheral Blood Mononuclear ◽  
Data Mining Algorithms ◽  
Blood Mononuclear Cells ◽  
Raman Spectral Data ◽  
Evaluation Of Data ◽  
Mining Algorithms ◽  
Raman Spectral

In this study Raman spectral data from peripheral blood mononuclear cells (PBMCs) is used for the competitive evaluation of three data-mining models in discriminating a highly pure population of T-cell lymphocytes from other myeloid cells within the PBMCs fraction.

scholarly journals Automated Phenotyping Tool for Identifying Developmental Language Disorder Cases in Health Systems Data (APT-DLD): A New Research Algorithm for Deployment in Large-Scale Electronic Health Record Systems

2020 ◽  
Vol 63 (9) ◽  
pp. 3019-3035
Author(s):  
Courtney E. Walters ◽  
Rachana Nitin ◽  
Katherine Margulis ◽  
Olivia Boorom ◽  
Daniel E. Gustavson ◽  
...  
Keyword(s):  
Data Mining ◽  
Large Scale ◽  
Language Disorder ◽  
Developmental Language Disorder ◽  
Replication Sample ◽  
Data Mining Algorithms ◽  
Discovery Sample ◽  
Automated Phenotyping ◽  
Mining Algorithms

Purpose Data mining algorithms using electronic health records (EHRs) are useful in large-scale population-wide studies to classify etiology and comorbidities ( Casey et al., 2016 ). Here, we apply this approach to developmental language disorder (DLD), a prevalent communication disorder whose risk factors and epidemiology remain largely undiscovered. Method We first created a reliable system for manually identifying DLD in EHRs based on speech-language pathologist (SLP) diagnostic expertise. We then developed and validated an automated algorithmic procedure, called, Automated Phenotyping Tool for identifying DLD cases in health systems data (APT-DLD), that classifies a DLD status for patients within EHRs on the basis of ICD (International Statistical Classification of Diseases and Related Health Problems) codes. APT-DLD was validated in a discovery sample ( N = 973) using expert SLP manual phenotype coding as a gold-standard comparison and then applied and further validated in a replication sample of N = 13,652 EHRs. Results In the discovery sample, the APT-DLD algorithm correctly classified 98% (concordance) of DLD cases in concordance with manually coded records in the training set, indicating that APT-DLD successfully mimics a comprehensive chart review. The output of APT-DLD was also validated in relation to independently conducted SLP clinician coding in a subset of records, with a positive predictive value of 95% of cases correctly classified as DLD. We also applied APT-DLD to the replication sample, where it achieved a positive predictive value of 90% in relation to SLP clinician classification of DLD. Conclusions APT-DLD is a reliable, valid, and scalable tool for identifying DLD cohorts in EHRs. This new method has promising public health implications for future large-scale epidemiological investigations of DLD and may inform EHR data mining algorithms for other communication disorders. Supplemental Material https://doi.org/10.23641/asha.12753578

scholarly journals Data mining, fuzzy AHP and TOPSIS for optimizing taxpayer supervision

2020 ◽  
Vol 18 (1) ◽  
pp. 75
Author(s):  
M. Jupri ◽  
Riyanarto Sarno
Keyword(s):  
Data Mining ◽  
Nearest Neighbor ◽  
Fuzzy Ahp ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Data Mining Algorithms ◽  
Using Data ◽  
Time Required ◽  
Mining Algorithms

The achievement of accepting optimal tax need effective and efficient tax supervision can be achieved by classifying taxpayer compliance to tax regulations. Considering this issue, this paper proposes the classification of taxpayer compliance using data mining algorithms; i.e. C4.5, Support Vector Machine, K-Nearest Neighbor, Naive Bayes, and Multilayer Perceptron based on the compliance of taxpayer data. The taxpayer compliance can be classified into four classes, which are (1) formal and material compliant taxpayers, (2) formal compliant taxpayers, (3) material compliant taxpayers, and (4) formal and material non-compliant taxpayers. Furthermore, the results of data mining algorithms are compared by using Fuzzy AHP and TOPSIS to determine the best performance classification based on the criteria of Accuracy, F-Score, and Time required. Selection of the taxpayer's priority for more detailed supervision at each level of taxpayer compliance is ranked using Fuzzy AHP and TOPSIS based on criteria of dataset variables. The results show that C4.5 is the best performance classification and achieves preference value of 0.998; whereas the MLP algorithm results from the lowest preference value of 0.131. Alternative taxpayer A233 is the top priority taxpayer with a preference value of 0.433; whereas alternative taxpayer A051 is the lowest priority taxpayer with a preference value of 0.036.

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