Introduction

He, Haibo

doi:10.1002/9781118646106.ch1

Cited by 43 publications

(35 citation statements)

References 42 publications

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“…We evaluated the algorithm with test-set AUC, area under the precision-recall curve (AUC-PR), sensitivity, specificity, and metrics that combine sensitivity and specificity that are commonly used on imbalanced classification problems: F-score and Youden index . We also present the positive predictive value and negative predictive value.…”

Section: Methodsmentioning

confidence: 99%

Machine Learning Analysis of Handgun Transactions to Predict Firearm Suicide Risk

et al. 2022

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Key Points Question Can handgun purchasing records, coupled with machine learning techniques, be used to forecast firearm suicide risk? Findings In this prognostic study of nearly 2 million individuals with handgun transaction records, among transactions classified in the riskiest 5%, close to 40% were associated with a purchaser who died by firearm suicide within 1 year. Among the small number of transactions with a random forest score of 0.95 and above, more than two-thirds were affiliated with a purchaser who died by firearm suicide within 1 year (24 of 35). Meaning This study suggests that passively collected administrative data on handgun transactions may be used to inform targeted interventions based on risk stratification.

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Section: Methodsmentioning

confidence: 99%

Machine Learning Analysis of Handgun Transactions to Predict Firearm Suicide Risk

et al. 2022

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“…While the selection of lesions from the Dermofit dataset does not present an extreme imbalance between benign and malignant tumours (≈29:20), when comparing between individual samples, this imbalance increases greatly (≈97:13 in the worst of cases). From this perspective, the present study chose to use evaluation metrics less susceptible to changes in sample balance [ 55 ], namely, Accuracy, Precision, Recall, the F1 Score, and the Area Under the precision–recall Curve (AUC). Each of these metrics, except for AUC, were calculated using confusion matrices, measuring the ratio of correctly classified individuals (True Positive & True Negative), as well as miss-classified individuals (False Positive & False Negative).…”

Section: Methodsmentioning

confidence: 99%

A Novel Approach for the Shape Characterisation of Non-Melanoma Skin Lesions Using Elliptic Fourier Analyses and Clinical Images

Courtenay

Barbero-García

Aramendi

et al. 2022

JCM

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The early detection of Non-Melanoma Skin Cancer (NMSC) is crucial to achieve the best treatment outcomes. Shape is considered one of the main parameters taken for the detection of some types of skin cancer such as melanoma. For NMSC, the importance of shape as a visual detection parameter is not well-studied. A dataset of 993 standard camera images containing different types of NMSC and benign skin lesions was analysed. For each image, the lesion boundaries were extracted. After an alignment and scaling, Elliptic Fourier Analysis (EFA) coefficients were calculated for the boundary of each lesion. The asymmetry of lesions was also calculated. Then, multivariate statistics were employed for dimensionality reduction and finally computational learning classification was employed to evaluate the separability of the classes. The separation between malignant and benign samples was successful in most cases. The best-performing approach was the combination of EFA coefficients and asymmetry. The combination of EFA and asymmetry resulted in a balanced accuracy of 0.786 and an Area Under Curve of 0.735. The combination of EFA and asymmetry for lesion classification resulted in notable success rates when distinguishing between benign and malignant lesions. In light of these results, skin lesions’ shape should be integrated as a fundamental part of future detection techniques in clinical screening.

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“…Additionally, the area under the receiver operating characteristic curve (ROC) is used to evaluate several thresholds between Recall and the false-positive rate (FPR), defined as FP/ (FP+TN). The accuracy of the detection results (F1-score) is assessed using the Precision vs Recall curve, which focuses on evaluating the performance of a classifier for different probability thresholds on the minority class (He and Ma, 2013).…”

Section: Evaluation Metricsmentioning

confidence: 99%

Simultaneous mass estimation and class classification of scrap metals using deep learning

Díaz‐Romero

Eynde

Sterkens

et al. 2022

Resources, Conservation and Recycling

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Introduction

Cited by 43 publications

References 42 publications

Machine Learning Analysis of Handgun Transactions to Predict Firearm Suicide Risk

Machine Learning Analysis of Handgun Transactions to Predict Firearm Suicide Risk

A Novel Approach for the Shape Characterisation of Non-Melanoma Skin Lesions Using Elliptic Fourier Analyses and Clinical Images

Simultaneous mass estimation and class classification of scrap metals using deep learning

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