An efficient machine learning approach for diagnosis of paraquat-poisoned patients

Hu, Lufeng; Hong, Guangliang; Ma, Jianshe; Wang, Xianqin; Chen, Huiling

doi:10.1016/j.compbiomed.2015.02.003

Cited by 134 publications

(64 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Previous studies [ 14 , 30 ] showed that the activation functions and hidden neurons have more or less impact on ELM performance. Therefore, these two factors were investigated in the following experiment.…”

Section: Resultsmentioning

confidence: 99%

“…As a result, the ELM learns more quickly and keeps fewer fine- tuning parameters than ANN, while maintaining excellent generalization performance. ELM has been applied in fields such as disease diagnosis [ 13 , 14 ], image quality assessment [ 15 ], face recognition [ 16 ], land cover classification [ 17 ] and hyperspectral images classification [ 18 ]. To the best of the authors’ knowledge, ELM has not yet been used in overweight modeling applications.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Using Blood Indexes to Predict Overweight Statuses: An Extreme Learning Machine-Based Approach

et al. 2015

Self Cite

View full text Add to dashboard Cite

The number of the overweight people continues to rise across the world. Studies have shown that being overweight can increase health risks, such as high blood pressure, diabetes mellitus, coronary heart disease, and certain forms of cancer. Therefore, identifying the overweight status in people is critical to prevent and decrease health risks. This study explores a new technique that uses blood and biochemical measurements to recognize the overweight condition. A new machine learning technique, an extreme learning machine, was developed to accurately detect the overweight status from a pool of 225 overweight and 251 healthy subjects. The group included 179 males and 297 females. The detection method was rigorously evaluated against the real-life dataset for accuracy, sensitivity, specificity, and AUC (area under the receiver operating characteristic (ROC) curve) criterion. Additionally, the feature selection was investigated to identify correlating factors for the overweight status. The results demonstrate that there are significant differences in blood and biochemical indexes between healthy and overweight people (p-value < 0.01). According to the feature selection, the most important correlated indexes are creatinine, hemoglobin, hematokrit, uric Acid, red blood cells, high density lipoprotein, alanine transaminase, triglyceride, and γ-glutamyl transpeptidase. These are consistent with the results of Spearman test analysis. The proposed method holds promise as a new, accurate method for identifying the overweight status in subjects.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using Blood Indexes to Predict Overweight Statuses: An Extreme Learning Machine-Based Approach

et al. 2015

Self Cite

View full text Add to dashboard Cite

show abstract

“…The major part of the dataset is specified for training the network (training data), and the quality of this process is evaluated by using the testing data. The training procedure is usually carried out by the "back propagation" (BP) method [61][62][63]. In this sense, the main effort of BP is to minimize the error performance (i.e., the difference between the actual and estimated outputs) through propagating on a backward path.…”

Section: Multilayer Perceptron (Mlp)mentioning

confidence: 99%

“…Most recently developed prediction techniques employed the computer science advances and intended to find a reliable solution in solving engineering and medical related problems. In this sense, the technique of extreme machine learning approaches [30][31][32]33], Harris hawks optimization [34,35], spatial adjacent histogram [36], fruit fly optimization [37], chaotic moth-flame optimization [38,39,40], multi-swarm whale optimizer [41], grey wolf optimization [42] can be mentioned. Such multi-disciplinary techniques are widely used as analysis tools in most complex engineering projects such as building information modelling [43,44], sustainable sediment management in hydropower [45], contractors' dynamic price competition in mega projects [46], emotion recognition and image sharing [47], wireless sensor networks [48,49], big data application [50,51], landslide prediction over a large region [52], Digital Neuromorphic Architecture [53].…”

Section: Introductionmentioning

confidence: 99%

Soft computing method for predicting pressure drop reduction in crude oil pipelines based on machine learning methods

Moayedi

Foong

Nguyen

2020

J Braz. Soc. Mech. Sci. Eng.

View full text Add to dashboard Cite

In the oil industry, the drag-reducing agent has been used to reduce turbulent friction of fluids. The main effort of this study is to examine the feasibility of four novel machine learning models, namely multilayer perceptron, M5Rules, decision table (DT), and trees M5P to estimate the percentage of drag reduction. Then, the mentioned methods are utilized to identify a relationship between the input and output parameters of the crude oil pipeline system. The parameter percentage of drag reduction was taken as the essential output. In contrast, the input parameters selected the flow rate of oil, polymer concentration, kind of polymer, temperature, as well as pipe diameter and roughness. The predicted results obtained by the tools mentioned above were evaluated according to several known statistical indices, namely coefficient of determination (R 2), mean absolute error (MAE), root mean squared error (RMSE), relative absolute error (RAE), and root relative squared error (RRSE) as well as novel ranking systems of color intensity rating and total ranking method. The training and testing results of the DT learning method for the

show abstract

“…Motivated by the excellent performance achieved by the ELM or KELM classifier on the disease diagnosis problems such as thyroid disease diagnosis [31], erythemato-squamous diseases diagnosis [32] and paraquat-poisoned patients diagnosis [33], in this study, an attempt was made to explore the potential of ELM and KELM in constructing an automatic diagnostic system for diagnosis of PD. Previous study [10,14,15,19,23] on PD diagnosis have proven that using dimension reduction before conducting the classification task can improve the diagnosis accuracy.…”

mentioning

confidence: 99%

An efficient hybrid kernel extreme learning machine approach for early diagnosis of Parkinson׳s disease

Chen

Wang²,

et al. 2016

Neurocomputing

238

View full text Add to dashboard Cite

Abstract:In this paper, we explore the potential of extreme learning machine (ELM) and kernel ELM (KELM) for early diagnosis of Parkinson's disease (PD). In the proposed method, the key parameters including the number of hidden neuron and type of activation function in ELM, and the constant parameter C and kernel parameter γ in KELM are investigated in detail. With the obtained optimal parameters, ELM and KELM manage to train the optimal predictive models for PD diagnosis. In order to further improve the performance of ELM and KELM models, feature selection techniques are implemented prior to the construction of the classification models. The effectiveness of the proposed method has been rigorously evaluated against the PD data set in terms of classification accuracy, sensitivity, specificity and the area under the ROC (receiver operating characteristic) curve (AUC). Compared to the existing methods in previous studies, the proposed method has achieved very promising classification accuracy via 10-fold cross-validation (CV) analysis, with the highest accuracy of 96.47% and average accuracy of 95.97% over 10 runs of 10-fold CV.

show abstract

An efficient machine learning approach for diagnosis of paraquat-poisoned patients

Cited by 134 publications

References 24 publications

Using Blood Indexes to Predict Overweight Statuses: An Extreme Learning Machine-Based Approach

Using Blood Indexes to Predict Overweight Statuses: An Extreme Learning Machine-Based Approach

Soft computing method for predicting pressure drop reduction in crude oil pipelines based on machine learning methods

An efficient hybrid kernel extreme learning machine approach for early diagnosis of Parkinson׳s disease

Contact Info

Product

Resources

About