Medical imbalanced data classification

Belarouci, Sara; Chikh, Mohammed Amine

doi:10.25046/aj020316

Cited by 38 publications

(15 citation statements)

References 41 publications

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“…A rebalancing strategy was performed to handle imbalanced data before developing the ML. We decided to use the Synthetic Minority Oversampling Technique (SMOTE) based on its effectiveness in the previous prediction model developments [ 29 , 30 ].…”

Section: Discussionmentioning

confidence: 99%

Mortality-Risk Prediction Model from Road-Traffic Injury in Drunk Drivers: Machine Learning Approach

Sirikul

Buawangpong

Sapbamrer

et al. 2021

IJERPH

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Background: Alcohol-related road-traffic injury is the leading cause of premature death in middle- and lower-income countries, including Thailand. Applying machine-learning algorithms can improve the effectiveness of driver-impairment screening strategies by legal limits. Methods: Using 4794 RTI drivers from secondary cross-sectional data from the Thai Governmental Road Safety Evaluation project in 2002–2004, the machine-learning models (Gradient Boosting Classifier: GBC, Multi-Layers Perceptrons: MLP, Random Forest: RF, K-Nearest Neighbor: KNN) and a parsimonious logistic regression (Logit) were developed for predicting the mortality risk from road-traffic injury in drunk drivers. The predictors included alcohol concentration level in blood or breath, driver characteristics and environmental factors. Results: Of 4974 drivers in the derived dataset, 4365 (92%) were surviving drivers and 429 (8%) were dead drivers. The class imbalance was rebalanced by the Synthetic Minority Oversampling Technique (SMOTE) into a 1:1 ratio. All models obtained good-to-excellent discrimination performance. The AUC of GBC, RF, KNN, MLP, and Logit models were 0.95 (95% CI 0.90 to 1.00), 0.92 (95% CI 0.87 to 0.97), 0.86 (95% CI 0.83 to 0.89), 0.83 (95% CI 0.78 to 0.88), and 0.81 (95% CI 0.75 to 0.87), respectively. MLP and GBC also had a good model calibration, visualized by the calibration plot. Conclusions: Our machine-learning models can predict road-traffic mortality risk with good model discrimination and calibration. External validation using current data is recommended for future implementation.

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Section: Discussionmentioning

confidence: 99%

Mortality-Risk Prediction Model from Road-Traffic Injury in Drunk Drivers: Machine Learning Approach

Sirikul

Buawangpong

Sapbamrer

et al. 2021

IJERPH

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“…e network traffic has shown nonlinear, nonstationary, and complex dynamic behavior. ere are many factors involved in describing the behavior or nature of this network at the broader level [1,48]. erefore, the network problem is treated as a high-dimensional problem.…”

Section: Modified Synergetic Neural Networkmentioning

confidence: 99%

iReTADS: An Intelligent Real-Time Anomaly Detection System for Cloud Communications Using Temporal Data Summarization and Neural Network

Lalotra

Kumar

Bhatt³

et al. 2022

Security and Communication Networks

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A new distributed environment at less financial expenditure on communication over the Internet is presented by cloud computing. In recent times, the increased number of users has made network traffic monitoring a difficult task. Although traffic monitoring and security problems are rising in parallel, there is a need to develop a new system for providing security and reducing network traffic. A new method, iReTADS, is proposed to reduce the network traffic using a data summarization technique and also provide network security through an effective real-time neural network. Although data summarization plays a significant role in data mining, still no real methods are present to assist the summary evaluation. Thus, it is a serious endeavor to present four metrics for data summarization with temporal features such as conciseness, information loss, interestingness, and intelligibility. In addition, a new metric time is also introduced for effective data summarization. Finally, a new neural network known as Modified Synergetic Neural Network (MSNN) on summarized datasets for detecting the real-time anomaly-behaved nodes in network and cloud is introduced. Experimental results reveal that the iReTADS can effectively monitor traffic and detect anomalies. It may further drive studies on controlling the outbreaks and controlling pandemics while studying medical datasets, which results in smart healthy cities.

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“…The problem of an imbalanced dataset is frequently present in healthcare applications. In particular, at least one class constitutes only a very small minority of the data [113].…”

Section: Research Challenges and Directionsmentioning

confidence: 99%

Classifying functional nuclear images with convolutional neural networks: a survey

et al. 2020

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Medical imbalanced data classification

Cited by 38 publications

References 41 publications

Mortality-Risk Prediction Model from Road-Traffic Injury in Drunk Drivers: Machine Learning Approach

Mortality-Risk Prediction Model from Road-Traffic Injury in Drunk Drivers: Machine Learning Approach

iReTADS: An Intelligent Real-Time Anomaly Detection System for Cloud Communications Using Temporal Data Summarization and Neural Network

Classifying functional nuclear images with convolutional neural networks: a survey

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