EEG-Driven Prediction Model of Oxcarbazepine Treatment Outcomes in Patients With Newly-Diagnosed Focal Epilepsy

Objective: Antiseizure medicine (ASM) is the first choice for patients with epilepsy. The choice of ASM is determined by the type of epilepsy or epileptic syndrome, which may not be suitable for certain patients. This initial choice of a particular drug affects the long-term prognosis of patients, so it is critical to select the appropriate ASMs based on the individual characteristics of a patient at the early stage of the disease. The purpose of this study is to develop a personalized prediction model to predict the probability of achieving seizure control in patients with focal epilepsy, which will help in providing a more precise initial medication to patients.Methods: Based on response to oxcarbazepine (OXC), enrolled patients were divided into two groups: seizure-free (52 patients), not seizure-free (NSF) (22 patients). We created models to predict patients' response to OXC monotherapy by combining Electroencephalogram (EEG) complexities and 15 clinical features. The prediction models were gradient boosting decision tree-Kolmogorov complexity (GBDT-KC) and gradient boosting decision tree-Lempel-Ziv complexity (GBDT-LZC). We also constructed two additional prediction models, support vector machine-Kolmogorov complexity (SVM-KC) and SVM-LZC, and these two models were compared with the GBDT models. The performance of the models was evaluated by calculating the accuracy, precision, recall, F1-score, sensitivity, specificity, and area under the curve (AUC) of these models.Results: The mean accuracy, precision, recall, F1-score, sensitivity, specificity, AUC of GBDT-LZC model after five-fold cross-validation were 81%, 84%, 91%, 87%, 91%, 64%, 81%, respectively. The average accuracy, precision, recall, F1-score, sensitivity, specificity, AUC of GBDT-KC model with five-fold cross-validation were 82%, 84%, 92%, 88%, 83%, 92%, 83%, respectively. We used the rank of absolute weights to separately calculate the features that have the most significant impact on the classification of the two models.Conclusion: (1) The GBDT-KC model has the potential to be used in the clinic to predict seizure-free with OXC monotherapy. (2). Electroencephalogram complexity, especially Kolmogorov complexity (KC) may be a potential biomarker in predicting the treatment efficacy of OXC in newly diagnosed patients with focal epilepsy.

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Predicting Change Prone Classes in Open Source Software

International Journal of Information Retrieval Research ◽

10.4018/ijirr.2018100101 ◽

2018 ◽

Vol 8 (4) ◽

pp. 1-23 ◽

Cited By ~ 2

Author(s):

Deepa Godara ◽

Amit Choudhary ◽

Rakesh Kumar Singh

Keyword(s):

Prediction Model ◽

Open Source ◽

Open Source Software ◽

Prediction Models ◽

New Technology ◽

Modern Technology ◽

Time Frequency ◽

Rigorous Testing ◽

Technology Changes ◽

Sensitivity Specificity

In today's world, the heart of modern technology is software. In order to compete with pace of new technology, changes in software are inevitable. This article aims at the association between changes and object-oriented metrics using different versions of open source software. Change prediction models can detect the probability of change in a class earlier in the software life cycle which would result in better effort allocation, more rigorous testing and easier maintenance of any software. Earlier, researchers have used various techniques such as statistical methods for the prediction of change-prone classes. In this article, some new metrics such as execution time, frequency, run time information, popularity and class dependency are proposed which can help in prediction of change prone classes. For evaluating the performance of the prediction model, the authors used Sensitivity, Specificity, and ROC Curve. Higher values of AUC indicate the prediction model gives significant accurate results. The proposed metrics contribute to the accurate prediction of change-prone classes.

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Forecasting of Carbon Emission in China Based on Gradient Boosting Decision Tree Optimized by Modified Whale Optimization Algorithm

Sustainability ◽

10.3390/su132112302 ◽

2021 ◽

Vol 13 (21) ◽

pp. 12302

Author(s):

Xiwen Cui ◽

Shaojun E ◽

Dongxiao Niu ◽

Bosong Chen ◽

Jiaqi Feng

Keyword(s):

Decision Tree ◽

Prediction Model ◽

Carbon Emission ◽

Historical Data ◽

Global Climate ◽

Optimization Methods ◽

Gradient Boosting ◽

Target Values ◽

Whale Optimization ◽

Practical Implications

As the global temperature continues to rise, people have become increasingly concerned about global climate change. In order to help China to effectively develop a carbon peak target completion plan, this paper proposes a carbon emission prediction model based on the improved whale algorithm-optimized gradient boosting decision tree, which combines four optimization methods and significantly improves the prediction accuracy. This paper uses historical data to verify the superiority of the gradient boosting tree prediction model optimized by the improved whale algorithm. In addition, this study also predicted the carbon emission values of China from 2020 to 2035 and compared them with the target values, concluding that China can accomplish the relevant target values, which suggests that this research has practical implications for China’s future carbon emission reduction policies.

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A Robust UWSN Handover Prediction System Using Ensemble Learning

Sensors ◽

10.3390/s21175777 ◽

2021 ◽

Vol 21 (17) ◽

pp. 5777

Author(s):

Esraa Eldesouky ◽

Mahmoud Bekhit ◽

Ahmed Fathalla ◽

Ahmad Salah ◽

Ahmed Ali

Keyword(s):

Decision Tree ◽

Ensemble Learning ◽

Prediction Accuracy ◽

Performance Metrics ◽

Prediction Models ◽

Sensor Nodes ◽

Wireless Sensor ◽

Water Current ◽

Gradient Boosting ◽

Marine Data

The use of underwater wireless sensor networks (UWSNs) for collaborative monitoring and marine data collection tasks is rapidly increasing. One of the major challenges associated with building these networks is handover prediction; this is because the mobility model of the sensor nodes is different from that of ground-based wireless sensor network (WSN) devices. Therefore, handover prediction is the focus of the present work. There have been limited efforts in addressing the handover prediction problem in UWSNs and in the use of ensemble learning in handover prediction for UWSNs. Hence, we propose the simulation of the sensor node mobility using real marine data collected by the Korea Hydrographic and Oceanographic Agency. These data include the water current speed and direction between data. The proposed simulation consists of a large number of sensor nodes and base stations in a UWSN. Next, we collected the handover events from the simulation, which were utilized as a dataset for the handover prediction task. Finally, we utilized four machine learning prediction algorithms (i.e., gradient boosting, decision tree (DT), Gaussian naive Bayes (GNB), and K-nearest neighbor (KNN)) to predict handover events based on historically collected handover events. The obtained prediction accuracy rates were above 95%. The best prediction accuracy rate achieved by the state-of-the-art method was 56% for any UWSN. Moreover, when the proposed models were evaluated on performance metrics, the measured evolution scores emphasized the high quality of the proposed prediction models. While the ensemble learning model outperformed the GNB and KNN models, the performance of ensemble learning and decision tree models was almost identical.

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A comparison of machine learning classifiers for dementia with Lewy bodies using miRNA expression data

BMC Medical Genomics ◽

10.1186/s12920-019-0607-3 ◽

2019 ◽

Vol 12 (1) ◽

Cited By ~ 2

Author(s):

Daichi Shigemizu ◽

Shintaro Akiyama ◽

Yuya Asanomi ◽

Keith A. Boroevich ◽

Alok Sharma ◽

...

Keyword(s):

Machine Learning ◽

Decision Tree ◽

Prediction Model ◽

Risk Prediction ◽

Mirna Expression ◽

Dementia With Lewy Bodies ◽

Target Genes ◽

Lewy Bodies ◽

Risk Prediction Model ◽

Gradient Boosting

Abstract Background Dementia with Lewy bodies (DLB) is the second most common subtype of neurodegenerative dementia in humans following Alzheimer’s disease (AD). Present clinical diagnosis of DLB has high specificity and low sensitivity and finding potential biomarkers of prodromal DLB is still challenging. MicroRNAs (miRNAs) have recently received a lot of attention as a source of novel biomarkers. Methods In this study, using serum miRNA expression of 478 Japanese individuals, we investigated potential miRNA biomarkers and constructed an optimal risk prediction model based on several machine learning methods: penalized regression, random forest, support vector machine, and gradient boosting decision tree. Results The final risk prediction model, constructed via a gradient boosting decision tree using 180 miRNAs and two clinical features, achieved an accuracy of 0.829 on an independent test set. We further predicted candidate target genes from the miRNAs. Gene set enrichment analysis of the miRNA target genes revealed 6 functional genes included in the DHA signaling pathway associated with DLB pathology. Two of them were further supported by gene-based association studies using a large number of single nucleotide polymorphism markers (BCL2L1: P = 0.012, PIK3R2: P = 0.021). Conclusions Our proposed prediction model provides an effective tool for DLB classification. Also, a gene-based association test of rare variants revealed that BCL2L1 and PIK3R2 were statistically significantly associated with DLB.

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Predicting the Insolvency of SMEs Using Technological Feasibility Assessment Information and Data Mining Techniques

Sustainability ◽

10.3390/su12239790 ◽

2020 ◽

Vol 12 (23) ◽

pp. 9790

Author(s):

Sanghoon Lee ◽

Keunho Choi ◽

Donghee Yoo

Keyword(s):

Data Mining ◽

Decision Tree ◽

Prediction Model ◽

Internal Control ◽

Prediction Models ◽

Influential Factors ◽

Financial Information ◽

Data Mining Techniques ◽

Feasibility Assessment ◽

Using Data

The government makes great efforts to maintain the soundness of policy funds raised by the national budget and lent to corporate. In general, previous research on the prediction of company insolvency has dealt with large and listed companies using financial information with conventional statistical techniques. However, small- and medium-sized enterprises (SMEs) do not have to undergo mandatory external audits, and the quality of accounting information is low due to weak internal control. To overcome this problem, we developed an insolvency prediction model for SMEs using data mining techniques and technological feasibility assessment information as non-financial information. We divided the dataset into two types of data based on three years of corporate age. The synthetic minority over-sampling technique (SMOTE) was used to solve the data imbalance that occurred at this time. Six insolvency prediction models were created using logistic regression, a decision tree, an artificial neural network, and an ensemble (i.e., boosting) of each algorithm. By applying a boosted decision tree, the best accuracies of 69.1% and 82.7% were derived, and by applying a decision tree, nine and seven influential factors affected the insolvency of SMEs established for fewer than three years and more than three years, respectively. In addition, we derived several insolvency rules for the two types of SMEs from the decision tree-based prediction model and proposed ways to enhance the health of loans given to potentially insolvent companies using these derived rules. The results of this study show that it is possible to predict SMEs’ insolvency using data mining techniques with technological feasibility assessment information and find meaningful rules related to insolvency.

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PredAmyl-MLP: Prediction of Amyloid Proteins Using Multilayer Perceptron

Computational and Mathematical Methods in Medicine ◽

10.1155/2020/8845133 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12

Author(s):

Yanjuan Li ◽

Zitong Zhang ◽

Zhixia Teng ◽

Xiaoyan Liu

Keyword(s):

Feature Extraction ◽

Feature Selection ◽

Prediction Model ◽

Multilayer Perceptron ◽

Type Ii Diabetes ◽

Cross Validation ◽

Experimental Results ◽

Type Ii ◽

Fold Cross Validation ◽

Better Than

Amyloid is generally an aggregate of insoluble fibrin; its abnormal deposition is the pathogenic mechanism of various diseases, such as Alzheimer’s disease and type II diabetes. Therefore, accurately identifying amyloid is necessary to understand its role in pathology. We proposed a machine learning-based prediction model called PredAmyl-MLP, which consists of the following three steps: feature extraction, feature selection, and classification. In the step of feature extraction, seven feature extraction algorithms and different combinations of them are investigated, and the combination of SVMProt-188D and tripeptide composition (TPC) is selected according to the experimental results. In the step of feature selection, maximum relevant maximum distance (MRMD) and binomial distribution (BD) are, respectively, used to remove the redundant or noise features, and the appropriate features are selected according to the experimental results. In the step of classification, we employed multilayer perceptron (MLP) to train the prediction model. The 10-fold cross-validation results show that the overall accuracy of PredAmyl-MLP reached 91.59%, and the performance was better than the existing methods.

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Towards Optimization of Boosting Models for Formation Lithology Identification

Mathematical Problems in Engineering ◽

10.1155/2019/5309852 ◽

2019 ◽

Vol 2019 ◽

pp. 1-13 ◽

Cited By ~ 1

Author(s):

Yunxin Xie ◽

Chenyang Zhu ◽

Yue Lu ◽

Zhengwei Zhu

Keyword(s):

Cross Validation ◽

Confusion Matrix ◽

Petroleum Engineering ◽

Gradient Boosting ◽

Gas Field ◽

Lithology Identification ◽

Extreme Gradient Boosting ◽

Evaluation Matrix ◽

Fold Cross Validation ◽

Geological Research

Lithology identification is an indispensable part in geological research and petroleum engineering study. In recent years, several mathematical approaches have been used to improve the accuracy of lithology classification. Based on our earlier work that assessed machine learning models on formation lithology classification, we optimize the boosting approaches to improve the classification ability of our boosting models with the data collected from the Daniudi gas field and Hangjinqi gas field. Three boosting models, namely, AdaBoost, Gradient Tree Boosting, and eXtreme Gradient Boosting, are evaluated with 5-fold cross validation. Regularization is applied to the Gradient Tree Boosting and eXtreme Gradient Boosting to avoid overfitting. After adapting the hyperparameter tuning approach on each boosting model to optimize the parameter set, we use stacking to combine the three optimized models to improve the classification accuracy. Results suggest that the optimized stacked boosting model has better performance concerning the evaluation matrix such as precision, recall, and f1 score compared with the single optimized boosting model. Confusion matrix also shows that the stacked model has better performance in distinguishing sandstone classes.

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Predictor Selection for Bacterial Vaginosis Diagnosis Using Decision Tree and Relief Algorithms

Applied Sciences ◽

10.3390/app10093291 ◽

2020 ◽

Vol 10 (9) ◽

pp. 3291

Author(s):

Jesús F. Pérez-Gómez ◽

Juana Canul-Reich ◽

José Hernández-Torruco ◽

Betania Hernández-Ocaña

Keyword(s):

Feature Selection ◽

Decision Tree ◽

Bacterial Vaginosis ◽

Cross Validation ◽

Performance Comparison ◽

Support Vector ◽

Ongoing Research ◽

Selection For ◽

Comparison Of The Results ◽

Fold Cross Validation

Requiring only a few relevant characteristics from patients when diagnosing bacterial vaginosis is highly useful for physicians as it makes it less time consuming to collect these data. This would result in having a dataset of patients that can be more accurately diagnosed using only a subset of informative or relevant features in contrast to using the entire set of features. As such, this is a feature selection (FS) problem. In this work, decision tree and Relief algorithms were used as feature selectors. Experiments were conducted on a real dataset for bacterial vaginosis with 396 instances and 252 features/attributes. The dataset was obtained from universities located in Baltimore and Atlanta. The FS algorithms utilized feature rankings, from which the top fifteen features formed a new dataset that was used as input for both support vector machine (SVM) and logistic regression (LR) algorithms for classification. For performance evaluation, averages of 30 runs of 10-fold cross-validation were reported, along with balanced accuracy, sensitivity, and specificity as performance measures. A performance comparison of the results was made between using the total number of features against using the top fifteen. These results found similar attributes from our rankings compared to those reported in the literature. This study is part of ongoing research that is investigating a range of feature selection and classification methods.

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Transmission Line Loss Prediction by Cross Validation and Gradient Boosting Decision Tree

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/440/3/032099 ◽

2020 ◽

Vol 440 ◽

pp. 032099

Author(s):

Jicheng Yu ◽

Feng Zhou ◽

Kai Zhu ◽

Changxi Yue ◽

Jiangchu Wang ◽

...

Keyword(s):

Decision Tree ◽

Transmission Line ◽

Cross Validation ◽

Gradient Boosting ◽

Loss Prediction ◽

Line Loss

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Property Rental Price Prediction Using the Extreme Gradient Boosting Algorithm

IJIIS: International Journal of Informatics and Information Systems ◽

10.47738/ijiis.v3i2.65 ◽

2020 ◽

Vol 3 (2) ◽

pp. 54-59

Author(s):

Marco Febriadi Kokasih ◽

Adi Suryaputra Paramita

Keyword(s):

Prediction Model ◽

Prediction Models ◽

Gradient Boosting ◽

Fair Price ◽

Price Prediction ◽

Online Marketplace ◽

Extreme Gradient Boosting ◽

Property Owners ◽

Boosting Algorithm

Online marketplace in the field of property renting like Airbnb is growing. Many property owners have begun renting out their properties to fulfil this demand. Determining a fair price for both property owners and tourists is a challenge. Therefore, this study aims to create a software that can create a prediction model for property rent price. Variable that will be used for this study is listing feature, neighbourhood, review, date and host information. Prediction model is created based on the dataset given by the user and processed with Extreme Gradient Boosting algorithm which then will be stored in the system. The result of this study is expected to create prediction models for property rent price for property owners and tourists consideration when considering to rent a property. In conclusion, Extreme Gradient Boosting algorithm is able to create property rental price prediction with the average of RMSE of 10.86 or 13.30%.

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