scholarly journals Comparison between Statistical Models and Machine Learning Methods on Classification for Highly Imbalanced Multiclass Kidney Data

Diagnostics ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 415 ◽  
Author(s):  
Bomi Jeong ◽  
Hyunjeong Cho ◽  
Jieun Kim ◽  
Soon Kil Kwon ◽  
SeungWoo Hong ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Statistical Models ◽  
Classification Performance ◽  
Health Examination ◽  
Learning Methods ◽  
Response Variable ◽  
Machine Learning Methods ◽  
Sensitivity Specificity ◽  
Weighted Values

This study aims to compare the classification performance of statistical models on highly imbalanced kidney data. The health examination cohort database provided by the National Health Insurance Service in Korea is utilized to build models with various machine learning methods. The glomerular filtration rate (GFR) is used to diagnose chronic kidney disease (CKD). It is calculated using the Modification of Diet in Renal Disease method and classified into five stages (1, 2, 3A and 3B, 4, and 5). Different CKD stages based on the estimated GFR are considered as six classes of the response variable. This study utilizes two representative generalized linear models for classification, namely, multinomial logistic regression (multinomial LR) and ordinal logistic regression (ordinal LR), as well as two machine learning models, namely, random forest (RF) and autoencoder (AE). The classification performance of the four models is compared in terms of accuracy, sensitivity, specificity, precision, and F1-Measure. To find the best model that classifies CKD stages correctly, the data are divided into a 10-fold dataset with the same rate for each CKD stage. Results indicate that RF and AE show better performance in accuracy than the multinomial and ordinal LR models when classifying the response variable. However, when a highly imbalanced dataset is modeled, the accuracy of the model performance can distort the actual performance. This occurs because accuracy is high even if a statistical model classifies a minority class into a majority class. To solve this problem in performance interpretation, we not only consider accuracy from the confusion matrix but also sensitivity, specificity, precision, and F-1 measure for each class. To present classification performance with a single value for each model, we calculate the macro-average and micro-weighted values for each model. We conclude that AE is the best model classifying CKD stages correctly for all performance indices.

scholarly journals Real-time prediction of inpatient length of stay for discharge prioritization

2015 ◽  
Vol 23 (e1) ◽  
pp. e2-e10 ◽  
Author(s):  
Sean Barnes ◽  
Eric Hamrock ◽  
Matthew Toerper ◽  
Sauleh Siddiqui ◽  
Scott Levin
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Health Information ◽  
Patient Flow ◽  
Supervised Machine Learning ◽  
Youden Index ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
High Resource ◽  
Sensitivity Specificity

Abstract Objective Hospitals are challenged to provide timely patient care while maintaining high resource utilization. This has prompted hospital initiatives to increase patient flow and minimize nonvalue added care time. Real-time demand capacity management (RTDC) is one such initiative whereby clinicians convene each morning to predict patients able to leave the same day and prioritize their remaining tasks for early discharge. Our objective is to automate and improve these discharge predictions by applying supervised machine learning methods to readily available health information. Materials and Methods The authors use supervised machine learning methods to predict patients’ likelihood of discharge by 2 p.m. and by midnight each day for an inpatient medical unit. Using data collected over 8000 patient stays and 20 000 patient days, the predictive performance of the model is compared to clinicians using sensitivity, specificity, Youden’s Index (i.e., sensitivity + specificity – 1), and aggregate accuracy measures. Results The model compared to clinician predictions demonstrated significantly higher sensitivity ( P  < .01), lower specificity ( P  < .01), and a comparable Youden Index ( P  > .10). Early discharges were less predictable than midnight discharges. The model was more accurate than clinicians in predicting the total number of daily discharges and capable of ranking patients closest to future discharge. Conclusions There is potential to use readily available health information to predict daily patient discharges with accuracies comparable to clinician predictions. This approach may be used to automate and support daily RTDC predictions aimed at improving patient flow.

scholarly journals Combining Multi-Dimensional Convolutional Neural Network (CNN) With Visualization Method for Detection of Aphis gossypii Glover Infection in Cotton Leaves Using Hyperspectral Imaging

2021 ◽  
Vol 12 ◽  
Author(s):  
Tianying Yan ◽  
Wei Xu ◽  
Jiao Lin ◽  
Long Duan ◽  
Pan Gao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hyperspectral Imaging ◽  
Aphis Gossypii ◽  
Classification Performance ◽  
Hyperspectral Images ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Conventional Machine ◽  
Rgb Images ◽  
3D Cnn

Cotton is a significant economic crop. It is vulnerable to aphids (Aphis gossypii Glovers) during the growth period. Rapid and early detection has become an important means to deal with aphids in cotton. In this study, the visible/near-infrared (Vis/NIR) hyperspectral imaging system (376–1044 nm) and machine learning methods were used to identify aphid infection in cotton leaves. Both tall and short cotton plants (Lumianyan 24) were inoculated with aphids, and the corresponding plants without aphids were used as control. The hyperspectral images (HSIs) were acquired five times at an interval of 5 days. The healthy and infected leaves were used to establish the datasets, with each leaf as a sample. The spectra and RGB images of each cotton leaf were extracted from the hyperspectral images for one-dimensional (1D) and two-dimensional (2D) analysis. The hyperspectral images of each leaf were used for three-dimensional (3D) analysis. Convolutional Neural Networks (CNNs) were used for identification and compared with conventional machine learning methods. For the extracted spectra, 1D CNN had a fine classification performance, and the classification accuracy could reach 98%. For RGB images, 2D CNN had a better classification performance. For HSIs, 3D CNN performed moderately and performed better than 2D CNN. On the whole, CNN performed relatively better than conventional machine learning methods. In the process of 1D, 2D, and 3D CNN visualization, the important wavelength ranges were analyzed in 1D and 3D CNN visualization, and the importance of wavelength ranges and spatial regions were analyzed in 2D and 3D CNN visualization. The overall results in this study illustrated the feasibility of using hyperspectral imaging combined with multi-dimensional CNN to detect aphid infection in cotton leaves, providing a new alternative for pest infection detection in plants.

Quantitative Assessment of Ataxia in Multiple Sclerosis Patients using Spatiotemporal Parameters: A Relief-Based Machine Learning Analysis

2021 ◽  
Author(s):  
Furkan Bilek ◽  
Ferhat Balgetir ◽  
Caner Feyzi Demir ◽  
Gökhan Alkan ◽  
Seda Arslan-Tuncer
Keyword(s):  
Machine Learning ◽  
Multiple Sclerosis ◽  
Sensitivity And Specificity ◽  
Classification Performance ◽  
Support Vector ◽  
Learning Methods ◽  
K Nearest Neighbors ◽  
Machine Learning Methods ◽  
Spatiotemporal Parameters ◽  
Multiple Sclerosis Patients

Abstract Background and Objective Multiple sclerosis (MS) is a chronic, progressive, and autoimmune disease of the central nervous system (CNS) characterized by inflammation, demyelination, and axonal injury. In patients with newly diagnosed MS (ndMS), ataxia can present either as mild or severe and can be difficult to diagnose in the absence of clinical disability. Such difficulties can be eliminated by using decision support systems supported by machine learning methods. The present study aimed to achieve early diagnosis of ataxia in ndMS patients by using machine learning methods with spatiotemporal parameters. Materials and Methods The prospective study included 32 ndMS patients with an Expanded Disability Status Scale (EDSS) score of≤2.0 and 32 healthy volunteers. A total of 14 parameters were elicited by using a Win-Track platform. The ndMS patients were differentiated from healthy individuals using multiple classifiers including Artificial Neural Network (ANN), Support Vector Machine (SVM), the k-nearest neighbors (K-NN) algorithm, and Decision Tree Learning (DTL). To improve the performance of the classification, a Relief-based feature selection algorithm was applied to select the subset that best represented the whole dataset. Performance evaluation was achieved based on several criteria such as Accuracy (ACC), Sensitivity (SN), Specificity (SP), and Precision (PREC). Results ANN had a higher classification performance compared to other classifiers, whereby it provided an accuracy, sensitivity, and specificity of 89, 87.8, 90.3% with the use of all parameters and provided the values of 93.7, 96.6%, and 91.1% with the use of parameters selected by the Relief algorithm, respectively. Significance To our knowledge, this is the first study of its kind in the literature to investigate the diagnosis of ataxia in ndMS patients by using machine learning methods with spatiotemporal parameters. The proposed method, i. e. Relief-based ANN method, successfully diagnosed ataxia by using a lower number of parameters compared to the numbers of parameters reported in clinical studies, thereby reducing the costs and increasing the performance of the diagnosis. The method also provided higher rates of accuracy, sensitivity, and specificity in the diagnosis of ataxia in ndMS patients compared to other methods. Taken together, these findings indicate that the proposed method could be helpful in the diagnosis of ataxia in minimally impaired ndMS patients and could be a pathfinder for future studies.

scholarly journals A comparison of logistic regression models with alternative machine learning methods to predict the risk of in-hospital mortality in emergency medical admissions via external validation

2018 ◽  
Vol 26 (1) ◽  
pp. 34-44 ◽  
Author(s):  
Muhammad Faisal ◽  
Andy Scally ◽  
Robin Howes ◽  
Kevin Beatson ◽  
Donald Richardson ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Hospital Mortality ◽  
Receiver Operating Characteristic Curve ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
External Validation ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Operating Characteristic Curve

We compare the performance of logistic regression with several alternative machine learning methods to estimate the risk of death for patients following an emergency admission to hospital based on the patients’ first blood test results and physiological measurements using an external validation approach. We trained and tested each model using data from one hospital ( n = 24,696) and compared the performance of these models in data from another hospital ( n = 13,477). We used two performance measures – the calibration slope and area under the receiver operating characteristic curve. The logistic model performed reasonably well – calibration slope: 0.90, area under the receiver operating characteristic curve: 0.847 compared to the other machine learning methods. Given the complexity of choosing tuning parameters of these methods, the performance of logistic regression with transformations for in-hospital mortality prediction was competitive with the best performing alternative machine learning methods with no evidence of overfitting.

scholarly journals Comparison of Deep-Learning and Conventional Machine-Learning Methods for the Automatic Recognition of the Hepatocellular Carcinoma Areas from Ultrasound Images

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3085 ◽  
Author(s):  
Raluca Brehar ◽  
Delia-Alexandrina Mitrea ◽  
Flaviu Vancea ◽  
Tiberiu Marita ◽  
Sergiu Nedevschi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hepatocellular Carcinoma ◽  
Deep Learning ◽  
Binary Classification ◽  
Classification Performance ◽  
Ultrasound Images ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Segmentation Accuracy ◽  
Conventional Machine

The emergence of deep-learning methods in different computer vision tasks has proved to offer increased detection, recognition or segmentation accuracy when large annotated image datasets are available. In the case of medical image processing and computer-aided diagnosis within ultrasound images, where the amount of available annotated data is smaller, a natural question arises: are deep-learning methods better than conventional machine-learning methods? How do the conventional machine-learning methods behave in comparison with deep-learning methods on the same dataset? Based on the study of various deep-learning architectures, a lightweight multi-resolution Convolutional Neural Network (CNN) architecture is proposed. It is suitable for differentiating, within ultrasound images, between the Hepatocellular Carcinoma (HCC), respectively the cirrhotic parenchyma (PAR) on which HCC had evolved. The proposed deep-learning model is compared with other CNN architectures that have been adapted by transfer learning for the ultrasound binary classification task, but also with conventional machine-learning (ML) solutions trained on textural features. The achieved results show that the deep-learning approach overcomes classical machine-learning solutions, by providing a higher classification performance.

scholarly journals Statistical and Machine Learning Methods for Software Fault Prediction Using CK Metric Suite: A Comparative Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
Yeresime Suresh ◽  
Lov Kumar ◽  
Santanu Ku. Rath
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Logistic Regression ◽  
Linear Regression ◽  
Object Oriented ◽  
Fault Prediction ◽  
Learning Methods ◽  
Software Fault Prediction ◽  
Machine Learning Methods ◽  
Software Fault

Experimental validation of software metrics in fault prediction for object-oriented methods using statistical and machine learning methods is necessary. By the process of validation the quality of software product in a software organization is ensured. Object-oriented metrics play a crucial role in predicting faults. This paper examines the application of linear regression, logistic regression, and artificial neural network methods for software fault prediction using Chidamber and Kemerer (CK) metrics. Here, fault is considered as dependent variable and CK metric suite as independent variables. Statistical methods such as linear regression, logistic regression, and machine learning methods such as neural network (and its different forms) are being applied for detecting faults associated with the classes. The comparison approach was applied for a case study, that is, Apache integration framework (AIF) version 1.6. The analysis highlights the significance of weighted method per class (WMC) metric for fault classification, and also the analysis shows that the hybrid approach of radial basis function network obtained better fault prediction rate when compared with other three neural network models.

ID: 3520519 LOGISTIC REGRESSION AND MACHINE LEARNING METHODS PREDICT CHOLEDOCHOLITHIASIS MORE ACCURATELY COMPARED TO CURRENT CRITERIA

2021 ◽  
Vol 93 (6) ◽  
pp. AB145-AB146
Author(s):  
John M. Azizian ◽  
Camellia Dalai ◽  
Harry Trieu ◽  
Anand Rajan ◽  
James H. Tabibian
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Learning Methods ◽  
Machine Learning Methods

scholarly journals Reproductive phasiRNAs in grasses are compositionally distinct from other classes of small RNAs

2018 ◽  
Author(s):  
Parth Patel ◽  
Sandra Mathioni ◽  
Atul Kakrana ◽  
Hagit Shatkay ◽  
Blake C. Meyers
Keyword(s):  
Machine Learning ◽  
Small Rnas ◽  
Structural Features ◽  
Classification Performance ◽  
List Type ◽  
Small Interfering Rnas ◽  
Sequencing Data ◽  
Learning Methods ◽  
Specific Sequence ◽  
Machine Learning Methods

Summary and keywordsLittle is known about the characteristics and function of reproductive phased, secondary, small interfering RNAs (phasiRNAs) in the Poaceae, despite the availability of significant genomic resources, experimental data, and a growing number of computational tools. We utilized machine-learning methods to identify sequence-based and structural features that distinguish phasiRNAs in rice and maize from other small RNAs (sRNAs).We developed Random Forest classifiers that can distinguish reproductive phasiRNAs from other sRNAs in complex sets of sequencing data, utilizing sequence-based (k-mers) and features describing position-specific sequence biases.The classification performance attained is >80% in accuracy, sensitivity, specificity, and positive predicted value. Feature selection identified important features in both ends of phasiRNAs. We demonstrated that phasiRNAs have strand specificity and position-specific nucleotide biases potentially influencing AGO sorting; we also predicted targets to infer functions of phasiRNAs, and computationally-assessed their sequence characteristics relative to other sRNAs.Our results demonstrate that machine-learning methods effectively identify phasiRNAs despite the lack of characteristic features typically present in precursor loci of other small RNAs, such as sequence conservation or structural motifs. The 5’-end features we identified provide insights into AGO-phasiRNA interactions; we describe a hypothetical model of competition for AGO loading between phasiRNAs of different nucleotide compositions.

scholarly journals Diagnose and Prediction of Different Machine Learning methods for Knee Osteoarthritis: a protocol for systematic review and meta-analysis

2021 ◽  
Author(s):  
yu zhou ◽  
Tong Mu ◽  
Xiaochuan Kong ◽  
Le Zhang
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Knee Osteoarthritis ◽  
Meta Analysis ◽  
Characteristic Curve ◽  
Joint Disease ◽  
Pool Data ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Sensitivity Specificity

Abstract Background: Knee osteoarthritis (OA) is a chronic and progressive joint disease with a higher contributor to global disability, mainly in the elderly and particularly in women. The available diagnostic approaches such as X-ray, computed tomography and magnetic resonance imaging have large precision errors and low sensitivity. Machine learning (ML) is the application of probabilistic algorithms to train a computational model to make predictions, it has great potential to become a valuable clinical diagnostic tool. This review aims to determine the diagnosis and prediction accuracy of different machine learning methods for Knee Osteoarthritis Methods: Two reviewers systematically searched Cochrane, PubMed, EMBASE, and Web of Science (last updated in June 2020) for eligible articles. To identify potentially missed publications, the reference lists of the final included studies were manually screened. Outcomes assessed were test characteristics such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (ROC). We will use the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool to assess the risk of bias and applicability. Two independent reviewers will conduct all procedures of study selection, data extraction, and methodological assessment. Any disagreements will be consulted with a third reviewer. RevMan 5.3 software and Stata V15.0 will be used to pool data and to carry out the meta-analysis if it is possible. Results: This systematic review will provide a high-quality synthesis of machine learning for diagnose of knee Osteoarthritis from various evaluation aspects including accuracy, sensitivity, specificity and AUC.Conclusion: The findings of this systematic review will provide latest evidence of diagnosis and prediction of different machine learning for patients with knee Osteoarthritis.Ethics and dissemination: No individual patient data will be used in this study; thus, no ethics approval is needed.Systematic review registration: PROSPERO CRD: 42019133305

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