A hybrid intelligent model for acute hypotensive episode prediction with large-scale data

Jiang, Dazhi; Tu, Geng; Jin, Donghui; Wu, Kaichao; Cheng, Liu; Zheng, Lin; Zhou, Teng

doi:10.1016/j.ins.2020.08.033

Cited by 25 publications

(5 citation statements)

References 29 publications

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“…This reveals the deep neural network can be applied for Internet fraud detection. The deep neural network is general, and can be extended to other applications, such as traffic flow forecasting [25]- [36], recommendation systems [37]- [39], medical image processing [40]- [44], intelligent computing [45]- [50].…”

Section: E Performance Evaluation and Discussionmentioning

confidence: 99%

Deep Learning Anti-Fraud Model for Internet Loan: Where We Are Going

Fang

Zhou

et al. 2021

IEEE Access

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Recently, Internet finance is increasingly popular. However, bad debt has become a serious threat to Internet financial companies. The fraud detection models commonly used in conventional financial companies is logistic regression. Although it is interpretable, the accuracy of the logistic regression still remains to be improved. This paper takes a large public loan dataset, e.g. Lending club, for example, to explore the potential of applying deep neural network for fraud detection. We first fill the missing values by a random forest. Then, an XGBoost algorithm is employed to select the most discriminate features. After that, we propose to use a synthetic minority oversampling technique to deal with the sample imbalance. With the preprocessed data, we design a deep neural network for Internet loan fraud detection. Extensive experiments have been conducted to demonstrate the outperformance of the deep neural network compared with the commonly-used models. Such a simple yet effective model may brighten the application of deep learning in anti-fraud for Internet loans, which would benefit the financial engineers in small and medium Internet financial companies.INDEX TERMS Internet finance, loan fraud detection, deep learning, financial model.

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Section: E Performance Evaluation and Discussionmentioning

confidence: 99%

Deep Learning Anti-Fraud Model for Internet Loan: Where We Are Going

Fang

Zhou

et al. 2021

IEEE Access

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“…Secondly, the BL-SVM system can learn to deal with the unavoidable absent metabolic feature value caused by patients moving, partial-volume effect, and overlapping among metabolites, which demonstrated the good robustness of our model. The BL-SVM learning system is general and can be extended to other applications, such as intelligent transportation systems [ 48 – 55 ], intelligent computing [ 56 , 57 ], and emotion computing [ 58 – 60 ].…”

Section: Discussionmentioning

confidence: 99%

Broad Learning Enhanced 1H-MRS for Early Diagnosis of Neuropsychiatric Systemic Lupus Erythematosus

Zhang

et al. 2020

Computational and Mathematical Methods in Medicine

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In this paper, we explore the potential of using the multivoxel proton magnetic resonance spectroscopy (1H-MRS) to diagnose neuropsychiatric systemic lupus erythematosus (NPSLE) with the assistance of a support vector machine broad learning system (BL-SVM). We retrospectively analysed 23 confirmed patients and 16 healthy controls, who underwent a 3.0 T magnetic resonance imaging (MRI) sequence with multivoxel 1H-MRS in our hospitals. One hundred and seventeen metabolic features were extracted from the multivoxel 1H-MRS image. Thirty-three metabolic features selected by the Mann-Whitney U test were considered to have a statistically significant difference ( p < 0.05 ). However, the best accuracy achieved by conventional statistical methods using these 33 metabolic features was only 77%. We turned to develop a support vector machine broad learning system (BL-SVM) to quantitatively analyse the metabolic features from 1H-MRS. Although not all the individual features manifested statistics significantly, the BL-SVM could still learn to distinguish the NPSLE from the healthy controls. The area under the receiver operating characteristic curve (AUC), the sensitivity, and the specificity of our BL-SVM in predicting NPSLE were 95%, 95.8%, and 93%, respectively, by 3-fold cross-validation. We consequently conclude that the proposed system effectively and efficiently working on limited and noisy samples may brighten a noinvasive in vivo instrument for early diagnosis of NPSLE.

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“…(3) a lack of individual performance measurement to explore the constituent technologies (elements) of this advanced intelligent hybrid model; (4) traditional research results lack effective rule interpretation capabilities, and are put at risk by their black box operations so as to encounter difficulties in decision making; and (5) the above knowledge gap has not been effectively filled. Most of the popular prediction models in recent years are composed of different modes or steps (hybrid or fusion), and their past performance has been obtained in the literature [14][15][16], where it is demonstrated that such an intelligent hybrid model is superior to a stand-alone model, since each classification technique has its strengths and weaknesses. In the past decades, scholars have devoted themselves to the application of intelligent hybrid models, which shows that advanced machine learning technology will still occupy a significant leading position in the future [15].…”

Section: Continuous Research Motivation and Research Originalitymentioning

confidence: 99%

A Study to Identify Long-Term Care Insurance Using Advanced Intelligent RST Hybrid Models with Two-Stage Performance Evaluation

Chen

Hung

Lin³

2023

Mathematics

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With the motivation of long-term care 2.0 plans, forecasting models to identify potential customers of long-term care insurance (LTCI) are an important and interesting issue. From the limited literature, most past researchers emphasize traditional statistics techniques to address this issue; however, these are lacking in some areas. For example, intelligent hybrid models for LTCI are lacking, performance measurement of components for hybrid models is lacking, and research results for interpretative capacities are lacking, resulting in a black box scenario and difficulty in making decisions, and the gap between identifying potential customers and constructing hybrid models is unbridged. To solve the shortcomings mentioned above, this study proposes some advanced intelligent single and hybrid models; the study object is LTCI customers. The proposed hybrid models were used on the experimental dataset collected from real insurance data and possess the following advantages: (1) The feature selection technique was used to simplify variables for the purpose of improving model performance. (2) The performance of hybrid models was evaluated against some machine learning methods, including rough set theory, decision trees, multilayer perceptron, support vector machine, genetic algorithm, random forest, logistic regression, and naive Bayes, and sensitivity analysis was performed in terms of accuracy, coverage, rules number, and standard deviation. (3) We used the C4.5 algorithm of decision trees and the LEM2 algorithm of rough sets to extract and provide valuably comprehensible decisional rules as decision-making references for the interested parties for their varied benefits. (4) We used post hoc testing to verify the significant difference in groups. Conclusively, this study effectively identifies potential customers for their key attributes and creates a decision rule set of knowledge for use as a reference when solving practical problems by forming a structured solution. This study is a new trial in the LTCI application field and realizes novel creative application values. Such a hybrid model is rarely seen in identifying LTCI potential customers; thus, the study has sufficient application contribution and managerial benefits to attract much concern from the interested parties.

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A hybrid intelligent model for acute hypotensive episode prediction with large-scale data

Cited by 25 publications

References 29 publications

Deep Learning Anti-Fraud Model for Internet Loan: Where We Are Going

Deep Learning Anti-Fraud Model for Internet Loan: Where We Are Going

Broad Learning Enhanced 1H-MRS for Early Diagnosis of Neuropsychiatric Systemic Lupus Erythematosus

A Study to Identify Long-Term Care Insurance Using Advanced Intelligent RST Hybrid Models with Two-Stage Performance Evaluation

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