Multi-label Classifier Performance Evaluation with Confusion Matrix

Krstinić, Damir; Braović, Maja; Šerić, Ljiljana; Božić-Štulic, Dunja

doi:10.5121/csit.2020.100801

Cited by 87 publications

(56 citation statements)

References 16 publications

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“…Figure 3 shows the results of the twelve-lead ECG classifier in the form of a multi-class confusion matrix, modified for multi-label problems according to [8]. This awards normalized scores to each class combination such that correct classifications are scored only on the leading diagonal, with misclassifications elsewhere.…”

Section: Resultsmentioning

confidence: 99%

An InceptionTime-Inspired Convolutional Neural Network to Detect Cardiac Abnormalities in Reduced-Lead ECG Data

Crocker

Costall

2021

2021 Computing in Cardiology (CinC)

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Cardiovascular disease is the leading cause of death worldwide. The twelve-lead electrocardiogram (ECG) is a common tool for diagnosing cardiac abnormalities, but its interpretation requires a trained cardiologist. Thus there is growing interest in automated ECG diagnosis, especially using fewer leads. Hence the PhysioNet-CinC Challenge 2021: Will two (leads) do? The University of Bath team (UoB HBC) developed InceptionTime-inspired deep convolutional neural networks, using parallel 1D convolutions of varying length, for twelve-, six-, four-, three-, and two-lead models. The twelve-lead model achieved a Challenge metric score of 0.35 on the test set, placing the University of Bath team 23rd out of 39 entries. Though the twelve-lead model performed best, three-lead performance was lower by only 0.25 %, suggesting potential for reliable reduced-lead diagnoses. Furthermore, the threelead model performed consistently better than the six-lead, highlighting the importance of selection of type of lead, not just their number.

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Section: Resultsmentioning

confidence: 99%

An InceptionTime-Inspired Convolutional Neural Network to Detect Cardiac Abnormalities in Reduced-Lead ECG Data

Crocker

Costall

2021

2021 Computing in Cardiology (CinC)

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“…Performance is calculated for each data instance and averaged over the whole dataset with example-based metrics. On the other hand, label-based metrics measure each label's performance separately before averaging across classes [ 59 ]. Hence, in this study, label-based macro-averaged accuracy, AUC, recall, precision, and F 1 score are used to evaluate the performance of the proposed model.…”

Section: Methodsmentioning

confidence: 99%

Lightweight Multireceptive Field CNN for 12-Lead ECG Signal Classification

Feyisa

Debelee

Ayano³

et al. 2022

Computational Intelligence and Neuroscience

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The electrical activity produced during the heartbeat is measured and recorded by an ECG. Cardiologists can interpret the ECG machine’s signals and determine the heart’s health condition and related causes of ECG signal abnormalities. However, cardiologist shortage is a challenge in both developing and developed countries. Moreover, the experience of a cardiologist matters in the accurate interpretation of the ECG signal, as the interpretation of ECG is quite tricky even for experienced doctors. Therefore, developing computer-aided ECG interpretation is required for its wide-reaching effect. 12-lead ECG generates a 1D signal with 12 channels among the well-known time-series data. Classical machine learning can develop automatic detection, but deep learning is more effective in the classification task. 1D-CNN is being widely used for CVDS detection from ECG datasets. However, adopting a deep learning model designed for computer vision can be problematic because of its massive parameters and the need for many samples to train. In many detection tasks ranging from semantic segmentation of medical images to time-series data classification, multireceptive field CNN has improved performance. Notably, the nature of the ECG dataset made performance improvement possible by using a multireceptive field CNN (MRF-CNN). Using MRF-CNN, it is possible to design a model that considers semantic context information within ECG signals with different sizes. As a result, this study has designed a multireceptive field CNN architecture for ECG classification. The proposed multireceptive field CNN architecture can improve the performance of ECG signal classification. We have achieved a 0.72 F 1 score and 0.93 AUC for 5 superclasses, a 0.46 F 1 score and 0.92 AUC for 20 subclasses, and a 0.31 F 1 score and 0.92 AUC for all the diagnostic classes of the PTB-XL dataset.

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“…Several formulas or equations in the performance evaluation measure are usually applied separately or in combination to get a better performance analysis perspective. Some of the calculations contained in the performance evaluation are as follows [46]. The precision method calculates the level of accuracy or accuracy of the results between user testing and system answers.…”

Section: Methodsmentioning

confidence: 99%

Effect of Random Splitting and Cross Validation for Indonesian Opinion Mining using Machine Learning Approach

Purba¹,

Ermatita²,

Abdiansah³

et al. 2022

IJACSA

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Opinion mining has been a prominent topic of research in Indonesia, however there are still many unanswered questions. The majority of past research has been on machine learning methods and models. A comparison of the effects of random splitting and cross-validation on processing performance is required. Text data is in Indonesian. The goal of this project is to use a machine learning model to conduct opinion mining on Indonesian text data using a random splitting and cross validation approach. This research consists of five stages: data collection, pre-processing, feature extraction, training & testing, and evaluation. Based on the experimental results, the TF-IDF feature is better than the Count-Vectorizer (CV) for Indonesian text. The best accuracy results are obtained by using TF-IDF as a feature and Support Vector Machine (SVM) as a classifier with cross validation implementation. The best accuracy reaches 81%. From the experimental results, it can also be seen that the implementation of cross validation can improve accuracy compared to the implementation of random splitting.

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Multi-label Classifier Performance Evaluation with Confusion Matrix

Cited by 87 publications

References 16 publications

An InceptionTime-Inspired Convolutional Neural Network to Detect Cardiac Abnormalities in Reduced-Lead ECG Data

An InceptionTime-Inspired Convolutional Neural Network to Detect Cardiac Abnormalities in Reduced-Lead ECG Data

Lightweight Multireceptive Field CNN for 12-Lead ECG Signal Classification

Effect of Random Splitting and Cross Validation for Indonesian Opinion Mining using Machine Learning Approach

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