Supporting Non-Experts' Awareness of Uncertainty

Beauxis-Aussalet, Emma; Arslanova, Elvira; Hardman, Lynda

doi:10.1145/2788412.2788432

Cited by 8 publications

(8 citation statements)

References 18 publications

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“…The Confusion Matrix is an N×N matrix practised for determining the performance of classification model for assumed set of test data [51]. It shows the values of true positives, true negatives, false positives and false negatives as illustrated in Figure 2.…”

Section: Confusion Matrixmentioning

confidence: 99%

Exploring Machine Learning Utilization on Influenza Pandemic Dataset

Hussain,

Fatima

2024

Preprint

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There must be an exact system for monitoring the influenza outbreaks to have an optimum solution for the recovery of infected people’s health. For reducing the spread of future outbreaks of influenza virus, forecasting plays an important role. Influenza a is type of disease which is transferred to human beings through pigs, found in animals. It became pandemic in Spain, approximately, 1/3rd of human population died and 1/4th of pig population. Again in 2009, influenza "A" caused millions of deaths, and spread like a pandemic rapidly. Variety of researches inspected data obtained from World Health Organization and local hospitals at country level. This research work is based on mathematical biology using data science techniques in the domain of machine learning. This research suggests a modeling scheme for influenza pandemic predictions, its different classifications and types such as H1N1, B-Victoria etc. via machine learning prediction and regression as well as classification algorithms such as Logistic Regression (LR), Support Vector Machines (SVM) using Linear, Polynomial and RBF kernels; Naïve Bayes (NB) and Random Forest (RF) method for the prediction of influenza disease and its outbreak, the influenza kind became pandemic with the infected populated area. After using various kernels in SVM algorithm, it is observed that Polynomial and Linear kernels have approximately the same accuracy scores, while RBF kernel was not best-fitted for the considered influenza datasets. As far as the overall performance is concerned, at average, RF has the highest accuracy score as 74% while the LR had also the better average score as 72% after RF. After applying the considered ML algorithms, Random Forest algorithm performed in well-effective manner and comparatively it was analyzed as the best-fitted algorithm for the considered datasets.

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Section: Confusion Matrixmentioning

confidence: 99%

Exploring Machine Learning Utilization on Influenza Pandemic Dataset

Hussain,

Fatima

2024

Preprint

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“…Precision is the ratio of true-positive data to the number of predicted positive data. The similarity value between the prediction and the actual measured value is known as accuracy (Beauxis-Aussalet & Hardman, 2014). Based on the division of the dataset, the evaluation of the IndoBERTweet classification report on the testing data is shown in Table 4.…”

Section: Classification Reportmentioning

confidence: 99%

“…Confusion matrix is an assessment technique to calculate the execution of characterization cycles or assess the error in classifiers (Beauxis-Aussalet & Hardman, 2014). We evaluate the performance of the model with the test data and display it on a confusion matrix with the predicted and true labels, as shown in Table 5.…”

Section: Confusion Matrixmentioning

confidence: 99%

Depression Detection of Users in Social Media X using IndoBERTweet

Fadhel,

Maharani

2024

SinkrOn

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According to the Ministry of Home Affairs, the population of Indonesia stands at 273 million, Indonesia has approximately 167 million active subscribers to virtual entertainment platforms, including YouTube, Facebook, Instagram, and Twitter. The use of online entertainment is huge, particularly on Twitter, and has been associated with mental health implications, such as depression. This research objective is to do a comprehensive study about the IndoBertweet deep learning framework to investigate the prevalence of depression in social media, focusing on Twitter. Utilizing the DASS-42, the research estimates depression levels based on user interactions and reactions to tweets. The results of this research showed that the IndoBERTweet method achieved an accuracy rate of 82% in detecting depression using Twitter data. This research highlights the importance of intervention strategies to support the mental health of social media users, emphasizing the importance of proactive measures in addressing mental well-being issues in the digital space.

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“…It is important to note that the calculation will be performed using the Caret package in R for the test datasets [50]. For further information on the confusion matrix, refer to [30,51].…”

Section: Metricsmentioning

confidence: 99%

Unraveling the Impact of Class Imbalance on Deep-Learning Models for Medical Image Classification

Hellín,

Olmedo,

Valledor

et al. 2024

Applied Sciences

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The field of image analysis with artificial intelligence has grown exponentially thanks to the development of neural networks. One of its most promising areas is medical diagnosis through lung X-rays, which are crucial for diseases like pneumonia, which can be mistaken for other conditions. Despite medical expertise, precise diagnosis is challenging, and this is where well-trained algorithms can assist. However, working with medical images presents challenges, especially when datasets are limited and unbalanced. Strategies to balance these classes have been explored, but understanding their local impact and how they affect model evaluation is still lacking. This work aims to analyze how a class imbalance in a dataset can significantly influence the informativeness of metrics used to evaluate predictions. It demonstrates that class separation in a dataset impacts trained models and is a strategy deserving more attention in future research. To achieve these goals, classification models using artificial and deep neural networks implemented in the R environment are developed. These models are trained using a set of publicly available images related to lung pathologies. All results are validated using metrics obtained from the confusion matrix to verify the impact of data imbalance on the performance of medical diagnostic models. The results raise questions about the procedures used to group classes in many studies, aiming to achieve class balance in imbalanced data and open new avenues for future research to investigate the impact of class separation in datasets with clinical pathologies.

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Supporting Non-Experts' Awareness of Uncertainty

Cited by 8 publications

References 18 publications

Exploring Machine Learning Utilization on Influenza Pandemic Dataset

Exploring Machine Learning Utilization on Influenza Pandemic Dataset

Depression Detection of Users in Social Media X using IndoBERTweet

Unraveling the Impact of Class Imbalance on Deep-Learning Models for Medical Image Classification

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