Efficient ensemble machine learning techniques for early prediction of diphtheria diseases based on clinical data

doi:10.19101/ijatee.2021.875402

Cited by 2 publications

(2 citation statements)

References 27 publications

(28 reference statements)

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“…2.1 SMOTEENN SMOTEENN ( SMOTE+ENN ) algorithm is a comprehensive sampling method that completes data sampling and data cleaning together [8] .SMOTE algorithm is a linear interpolation method to sample minority class samples in data, but it can easily lead to overfitting and sample overlap problems. ENN (edited nearest neighbours) is a data cleaning method, for a majority class sample point, if more than half of the K points in its vicinity are minority class sample points, then this majority class sample point is removed, so that the problem of positive and negative sample overlap can be solved.…”

Section: Related Technology Introductionmentioning

confidence: 99%

Research on network intrusion detection based on SMOTEENN and improved CatBoost algorithm

Huoming,

Yinhui,

Pinglan

et al. 2023

Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023)

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To address the problems of network intrusion data imbalance and prediction accuracy, a network intrusion detection model based on SMOTEENN ( Synthetic Minority Oversampling Technique Edited Nearest Neighbor ) and improved CatBoost algorithm is proposed, and SMOTEENN sampling is used to solve the data imbalance problem from the data level. The original CatBoost algorithm loss function is replaced with Focal Loss loss function to enhance the classification performance of CatBoost algorithm from the level of sample classification difficulty. And the Bayesian optimization algorithm is used to realize the CatBoost hyperparameter combination seeking. The network intrusion dataset NSL-KDD ( National Security Letter Knowledge Discovery in Database ) is input to the improved model and compared with other similar algorithms. The results show that the proposed method in this paper is optimal in terms of accuracy, F1-sacore, and AP value, To provide new ideas for network intrusion algorithm research.

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Section: Related Technology Introductionmentioning

confidence: 99%

Research on network intrusion detection based on SMOTEENN and improved CatBoost algorithm

Huoming,

Yinhui,

Pinglan

et al. 2023

Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023)

View full text Add to dashboard Cite

show abstract

“…In many cases, patients remain unaware of their conditions until they reach advanced and often irreparable stages of the disease. This situation indicating the pressing need for early detection methods that can significantly enhance the chances of successful treatment and recovery [9,10].…”

Section: Introductionmentioning

confidence: 99%

Machine learning in disease detection: a review of advancements, challenges, and implications for healthcare

2023

TIS

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The integration of machine learning into healthcare, particularly in disease detection, has the potential to transform patient outcomes and alleviate healthcare burdens. This specialized review explores the vast landscape of literature surrounding the application of machine learning in disease detection. Highlighted advantages include early detection, accuracy, personalized medicine, reduction in healthcare costs, and significant public health impact. However, challenges such as data quality, interpretability, data privacy, and integration into clinical practice persist. As machine learning techniques evolve to address these challenges, their role in disease detection is poised to become more integral to modern healthcare, promising better healthcare delivery.

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Efficient ensemble machine learning techniques for early prediction of diphtheria diseases based on clinical data

Cited by 2 publications

References 27 publications

Research on network intrusion detection based on SMOTEENN and improved CatBoost algorithm

Research on network intrusion detection based on SMOTEENN and improved CatBoost algorithm

Machine learning in disease detection: a review of advancements, challenges, and implications for healthcare

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