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Article: Support Vector Machines for Differential Prediction.

Kuusisto, Finn / Santos Costa, Vitor / Nassif, Houssam / Burnside, Elizabeth / Page, David / Shavlik, Jude

Machine learning and knowledge discovery in databases : European Conference, ECML PKDD ... : proceedings. ECML PKDD (Conference)

2015 Volume 8725, Page(s) 50–65

Abstract: Machine learning is continually being applied to a growing set of fields, including ... machine learning approaches and, in particular, there is growing interest in ...

Abstract	Machine learning is continually being applied to a growing set of fields, including the social sciences, business, and medicine. Some fields present problems that are not easily addressed using standard machine learning approaches and, in particular, there is growing interest in
Language	English
Publishing date	2015-04-20
Publishing country	Germany
Document type	Journal Article
DOI	10.1007/978-3-662-44851-9_4
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Article ; Online: Prediction of Differential Pharmacologic Response in Chronic Pain Using Functional Neuroimaging Biomarkers and a Support Vector Machine Algorithm: An Exploratory Study.

Ichesco, Eric / Peltier, Scott J / Mawla, Ishtiaq / Harper, Daniel E / Pauer, Lynne / Harte, Steven E / Clauw, Daniel J / Harris, Richard E

Arthritis & rheumatology (Hoboken, N.J.)

2021 Volume 73, Issue 11, Page(s) 2127–2137

Abstract: ... involved in pain processing. A support vector machine algorithm was used to classify FM patients ... of machine learning in pain prognosis and treatment prediction. ... Objective: There is increasing demand for prediction of chronic pain treatment outcomes using ...

Abstract	Objective: There is increasing demand for prediction of chronic pain treatment outcomes using machine-learning models, in order to improve suboptimal pain management. In this exploratory study, we used baseline brain functional connectivity patterns from chronic pain patients with fibromyalgia (FM) to predict whether a patient would respond differentially to either milnacipran or pregabalin, 2 drugs approved by the US Food and Drug Administration for the treatment of FM. Methods: FM patients participated in 2 separate double-blind, placebo-controlled crossover studies, one evaluating milnacipran (n = 15) and one evaluating pregabalin (n = 13). Functional magnetic resonance imaging during rest was performed before treatment to measure intrinsic functional brain connectivity in several brain regions involved in pain processing. A support vector machine algorithm was used to classify FM patients as responders, defined as those with a ≥20% improvement in clinical pain, to either milnacipran or pregabalin. Results: Connectivity patterns involving the posterior cingulate cortex (PCC) and dorsolateral prefrontal cortex (DLPFC) individually classified pregabalin responders versus milnacipran responders with 77% accuracy. Performance of this classification improved when both PCC and DLPFC connectivity patterns were combined, resulting in a 92% classification accuracy. These results were not related to confounding factors, including head motion, scanner sequence, or hardware status. Connectivity patterns failed to differentiate drug nonresponders across the 2 studies. Conclusion: Our findings indicate that brain functional connectivity patterns used in a machine-learning framework differentially predict clinical response to pregabalin and milnacipran in patients with chronic pain. These findings highlight the promise of machine learning in pain prognosis and treatment prediction.
MeSH term(s)	Adult ; Analgesics/therapeutic use ; Biomarkers ; Brain/diagnostic imaging ; Chronic Pain/diagnostic imaging ; Chronic Pain/drug therapy ; Cross-Over Studies ; Double-Blind Method ; Female ; Fibromyalgia/diagnostic imaging ; Fibromyalgia/drug therapy ; Humans ; Magnetic Resonance Imaging ; Middle Aged ; Milnacipran/therapeutic use ; Neuroimaging ; Pregabalin/therapeutic use ; Support Vector Machine ; Treatment Outcome ; Young Adult
Chemical Substances	Analgesics ; Biomarkers ; Pregabalin (55JG375S6M) ; Milnacipran (G56VK1HF36)
Language	English
Publishing date	2021-09-22
Publishing country	United States
Document type	Clinical Trial ; Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2756371-6
ISSN	2326-5205 ; 2326-5191
ISSN (online)	2326-5205
ISSN	2326-5191
DOI	10.1002/art.41781
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Article: Machine Learning for Clinical Decision Support of Acute Streptococcal Pharyngitis: A Pilot Study.

Hoffer, Oshrit / Cohen, Moriya / Gerstein, Maya / Shkalim Zemer, Vered / Richenberg, Yael / Nathanson, Shay / Avner Cohen, Herman

The Israel Medical Association journal : IMAJ

2024 Volume 26, Issue 5, Page(s) 299–303

Abstract: ... All patients underwent a McIsaac score evaluation. A linear support vector machine algorithm was used ... for classification.: Results: The machine learning algorithm resulted in a positive predictive value of 80.6 % (27 ... Conclusions: Applying the machine-learning strategy resulted in a high positive predictive value ...

Abstract	Background: Group A Streptococcus (GAS) is the predominant bacterial pathogen of pharyngitis in children. However, distinguishing GAS from viral pharyngitis is sometimes difficult. Unnecessary antibiotic use contributes to unwanted side effects, such as allergic reactions and diarrhea. It also may increase antibiotic resistance. Objectives: To evaluate the effect of a machine learning algorithm on the clinical evaluation of bacterial pharyngitis in children. Methods: We assessed 54 children aged 2-17 years who presented to a primary healthcare clinic with a sore throat and fever over 38°C from 1 November 2021 to 30 April 2022. All children were tested with a streptococcal rapid antigen detection test (RADT). If negative, a throat culture was performed. Children with a positive RADT or throat culture were considered GAS-positive and treated antibiotically for 10 days, as per guidelines. Children with negative RADT tests throat cultures were considered positive for viral pharyngitis. The children were allocated into two groups: Group A streptococcal pharyngitis (GAS-P) (n=36) and viral pharyngitis (n=18). All patients underwent a McIsaac score evaluation. A linear support vector machine algorithm was used for classification. Results: The machine learning algorithm resulted in a positive predictive value of 80.6 % (27 of 36) for GAS-P infection. The false discovery rates for GAS-P infection were 19.4 % (7 of 36). Conclusions: Applying the machine-learning strategy resulted in a high positive predictive value for the detection of streptococcal pharyngitis and can contribute as a medical decision aid in the diagnosis and treatment of GAS-P.
MeSH term(s)	Humans ; Pharyngitis/microbiology ; Pharyngitis/diagnosis ; Child ; Pilot Projects ; Streptococcal Infections/diagnosis ; Streptococcal Infections/drug therapy ; Child, Preschool ; Male ; Female ; Machine Learning ; Streptococcus pyogenes/isolation & purification ; Adolescent ; Decision Support Systems, Clinical ; Anti-Bacterial Agents/therapeutic use ; Anti-Bacterial Agents/administration & dosage ; Acute Disease ; Diagnosis, Differential ; Algorithms
Chemical Substances	Anti-Bacterial Agents
Language	English
Publishing date	2024-05-13
Publishing country	Israel
Document type	Journal Article
ZDB-ID	2008291-5
ISSN	1565-1088 ; 0021-2180
ISSN	1565-1088 ; 0021-2180
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Article: Combined measurement of serum zinc with PSA ameliorates prostate cancer screening efficiency via support vector machine algorithms.

Wu, Muyu / Zhang, Yucan / Zhang, Xiaoqun / Lin, Xiaozhu / Ding, Qiaoqiao / Li, Peiyong

Heliyon

2024 Volume 10, Issue 2, Page(s) e24292

Abstract: ... Sigmoid function provided substantial accuracy in preclinical risk prediction of developing ... differential screening of PCa. Meanwhile, our established PI stratification system based on SVM model and ...

Abstract	Background: Early screening of prostate cancer (PCa) is pivotal but challenging in the clinical scenario due to the phenomena of false positivity or false negativity of some serological evaluations, e.g. PSA testing. Decline of serum Zn Methods: A total of 858 male patients with prostate disorders and 345 healthy male controls were enrolled. Patients' data included 4 variables and serum Zn Results: In SVM model, the mean AUC of the ROC with the quartet of variables was approximately 84% for PCa diagnosis, whereas the mean AUC of the ROCs with tPSA, fPSA, [Zn Conclusion: Findings from our present study demonstrated that index combination via SVM algorithms may well facilitate clinicians in early differential screening of PCa. Meanwhile, our established PI stratification system based on SVM model and Sigmoid function provided substantial accuracy in preclinical risk prediction of developing prostate cancer.
Language	English
Publishing date	2024-01-08
Publishing country	England
Document type	Journal Article
ZDB-ID	2835763-2
ISSN	2405-8440
ISSN	2405-8440
DOI	10.1016/j.heliyon.2024.e24292
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Article: Spatial prediction of rainfall-induced shallow landslides using hybrid integration approach of Least-Squares Support Vector Machines and differential evolution optimization: a case study in Central Vietnam

Tien Bui, Dieu / Binh Thai Pham / Nhat-Duc Hoang / Quoc Phi Nguyen

International journal of digital earth. 2016 Nov. 1, v. 9, no. 11

2016

Abstract: ... optimization and Least-Squares Support Vector Machines for shallow landslide prediction, named as DEâLSSVM ... with those obtained from other methods, Support Vector Machines, Multilayer Perceptron Neural Networks, and J48 ... at hand; therefore, the proposed model can be a promising tool for spatial prediction of rainfall-induced ...

Abstract	This study represents a hybrid intelligence approach based on the differential evolution optimization and Least-Squares Support Vector Machines for shallow landslide prediction, named as DEâLSSVM SLP. The LSSVM is used to establish a landslide prediction model whereas the DE is adopted to search the optimal tuning parameters of the LSSVM model. In this research, a GIS database with 129 historical landslide records in the Quy Hop area (Central Vietnam) has been collected to establish the hybrid model. The receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to assess the performance of the newly constructed model. Experimental results show that the proposed model has high performances with approximately 82% of AUCs on both training and validating datasets. The modelâs results were compared with those obtained from other methods, Support Vector Machines, Multilayer Perceptron Neural Networks, and J48 Decision Trees. The result comparison demonstrates that the DEâLSSVM SLP deems best suited for the dataset at hand; therefore, the proposed model can be a promising tool for spatial prediction of rainfall-induced shallow landslides for the study area.
Keywords	case studies ; data collection ; databases ; decision support systems ; geographic information systems ; landslides ; least squares ; neural networks ; prediction ; support vector machines ; Vietnam
Language	English
Dates of publication	2016-1101
Size	p. 1077-1097.
Publishing place	Taylor & Francis
Document type	Article
ISSN	1753-8955
DOI	10.1080/17538947.2016.1169561
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Predicting acute clinical deterioration with interpretable machine learning to support emergency care decision making.

Boulitsakis Logothetis, Stelios / Green, Darren / Holland, Mark / Al Moubayed, Noura

Scientific reports

2023 Volume 13, Issue 1, Page(s) 13563

Abstract: ... decision trees, and support vector machines for predicting imminent clinical deterioration for patients based ... to the hospital. We apply state-of-the-art machine learning methods to predict early patient deterioration, based ... a systematised approach to data-driven risk modelling to obtain clinically applicable support tools. ...

Abstract	The emergency department (ED) is a fast-paced environment responsible for large volumes of patients with varied disease acuity. Operational pressures on EDs are increasing, which creates the imperative to efficiently identify patients at imminent risk of acute deterioration. The aim of this study is to systematically compare the performance of machine learning algorithms based on logistic regression, gradient boosted decision trees, and support vector machines for predicting imminent clinical deterioration for patients based on cross-sectional patient data extracted from electronic patient records (EPR) at the point of entry to the hospital. We apply state-of-the-art machine learning methods to predict early patient deterioration, based on their first recorded vital signs, observations, laboratory results, and other predictors documented in the EPR. Clinical deterioration in this study is measured by in-hospital mortality and/or admission to critical care. We build on prior work by incorporating interpretable machine learning and fairness-aware modelling, and use a dataset comprising 118, 886 unplanned admissions to Salford Royal Hospital, UK, to systematically compare model variations for predicting mortality and critical care utilisation within 24 hours of admission. We compare model performance to the National Early Warning Score 2 (NEWS2) and yield up to a 0.366 increase in average precision, up to a [Formula: see text] reduction in daily alert rate, and a median 0.599 reduction in differential bias amplification across the protected demographics of age and sex. We use Shapely Additive exPlanations to justify the models' outputs, verify that the captured data associations align with domain knowledge, and pair predictions with the causal context of each patient's most influential characteristics. Introducing our modelling to clinical practice has the potential to reduce alert fatigue and identify high-risk patients with a lower NEWS2 that might be missed currently, but further work is needed to trial the models in clinical practice. We encourage future research to follow a systematised approach to data-driven risk modelling to obtain clinically applicable support tools.
MeSH term(s)	Humans ; Clinical Deterioration ; Cross-Sectional Studies ; Emergency Medical Services ; Machine Learning ; Decision Making
Language	English
Publishing date	2023-08-21
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-023-40661-0
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Article ; Online: Soft-sensing modeling of mother liquor concentration in the evaporation process based on reduced robust least-squares support-vector machine.

Qian, Xiaoshan / Xu, Lisha / Yuan, Xinmei

Mathematical biosciences and engineering : MBE

2023 Volume 20, Issue 11, Page(s) 19941–19962

Abstract: ... the efficiency of the soft sensing model. The reduced robust least-squares support-vector machine (LSSVM ... with its commendable predictive performance, is used for modeling and predicting the principal components. Concurrently ... an improved Pattern Search-Differential Evolution (PS-DE) algorithm is proposed for optimizing the pivotal ...

Abstract	The evaporation process is vital in alumina production, with mother liquor concentration serving as a critical control parameter. To address the challenge of online detection, we propose the introduction of a soft measurement strategy. First, due to the significant fluctuations in the production process variables and inter-variable coupling, comprehensive grey correlation analysis and kernel principal component analysis are employed to reduce the input dimension and computational complexity of the data, enhancing the efficiency of the soft sensing model. The reduced robust least-squares support-vector machine (LSSVM), with its commendable predictive performance, is used for modeling and predicting the principal components. Concurrently, an improved Pattern Search-Differential Evolution (PS-DE) algorithm is proposed for optimizing the pivotal parameters of the LSSVM network. Lastly, on-site industrial data validation indicates that the new model offers superior tracking capabilities and heightened accuracy. It is deemed aptly suitable for the online detection of mother liquor concentration.
Language	English
Publishing date	2023-12-05
Publishing country	United States
Document type	Journal Article
ZDB-ID	2265126-3
ISSN	1551-0018 ; 1551-0018
ISSN (online)	1551-0018
ISSN	1551-0018
DOI	10.3934/mbe.2023883
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Article ; Online: Aerodynamic System Machine Learning Modeling with Gray Wolf Optimization Support Vector Regression and Instability Identification Strategy of Wavelet Singular Spectrum.

Zhang, Mingming / Kong, Pan / Xia, Aiguo / Tuo, Wei / Lv, Yongzhao / Wang, Shaohong

Biomimetics (Basel, Switzerland)

2023 Volume 8, Issue 2

Abstract: ... algorithm optimized support vector regression (WT-GWO-SVR) is proposed, which breaks through the fusion ... to train support vector regression. It is shown that the proposed WT-GWO-SVR hybrid model has a better ... such as differential evolution and particle swarm optimization. In order to further improve the prediction accuracy ...

Abstract	The prediction of a stall precursor in an axial compressor is the basic guarantee to the stable operation of an aeroengine. How to predict and intelligently identify the instability of the system in advance is of great significance to the safety performance and active control of the aeroengine. In this paper, an aerodynamic system modeling method combination with the wavelet transform and gray wolf algorithm optimized support vector regression (WT-GWO-SVR) is proposed, which breaks through the fusion technology based on the feature correlation of chaotic data. Because of the chaotic characteristic represented by the sequence, the correlation-correlation (C-C) algorithm is adopted to reconstruct the phase space of the spatial modal. On the premise of finding out the local law of the dynamic system variety, the machine learning method is applied to model the reconstructed low-frequency components and high-frequency components, respectively. As the key part, the parameters of the SVR model are optimized by the gray wolf optimization algorithm (GWO) from the biological view inspired by the predatory behavior of gray wolves. In the definition of the hunting behaviors of gray wolves by mathematical equations, it is superior to algorithms such as differential evolution and particle swarm optimization. In order to further improve the prediction accuracy of the model, the multi-resolution and equivalent frequency distribution of the wavelet transform (WT) are used to train support vector regression. It is shown that the proposed WT-GWO-SVR hybrid model has a better prediction accuracy and reliability with the wavelet reconstruction coefficients as the inputs. In order to effectively identify the sign of the instability in the modeling system, a wavelet singular information entropy algorithm is proposed to detect the stall inception. By using the three sigma criteria as the identification strategy, the instability early warning can be given about 102r in advance, which is helpful for the active control.
Language	English
Publishing date	2023-03-23
Publishing country	Switzerland
Document type	Journal Article
ISSN	2313-7673
ISSN (online)	2313-7673
DOI	10.3390/biomimetics8020132
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Article: The application of weighted gene co-expression network analysis and support vector machine learning in the screening of Parkinson's disease biomarkers and construction of diagnostic models.

Cai, Lijun / Tang, Shuang / Liu, Yin / Zhang, Yingwan / Yang, Qin

Frontiers in molecular neuroscience

2023 Volume 16, Page(s) 1274268

Abstract: ... Support Vector Machine (SVM) algorithm for screening biomarkers and constructing a diagnostic model ... provide strong support for early diagnosis of Parkinson's disease and offer new opportunities ... SVM algorithm.: Results: The prediction model demonstrated an AUC greater than 0.8 in the training ...

Abstract	Background: This study aims to utilize Weighted Gene Co-expression Network Analysis (WGCNA) and Support Vector Machine (SVM) algorithm for screening biomarkers and constructing a diagnostic model for Parkinson's disease. Methods: Firstly, we conducted WGCNA analysis on gene expression data from Parkinson's disease patients and control group using three GEO datasets (GSE8397, GSE20163, and GSE20164) to identify gene modules associated with Parkinson's disease. Then, key genes with significantly differential expression from these gene modules were selected as candidate biomarkers and validated using the GSE7621 dataset. Further functional analysis revealed the important roles of these genes in processes such as immune regulation, inflammatory response, and cell apoptosis. Based on these findings, we constructed a diagnostic model by using the expression data of FLT1, ATP6V0E1, ATP6V0E2, and H2BC12 as inputs and training and validating the model using SVM algorithm. Results: The prediction model demonstrated an AUC greater than 0.8 in the training, test, and validation sets, thereby validating its performance through SMOTE analysis. These findings provide strong support for early diagnosis of Parkinson's disease and offer new opportunities for personalized treatment and disease management. Conclusion: In conclusion, the combination of WGCNA and SVM holds potential in biomarker screening and diagnostic model construction for Parkinson's disease.
Language	English
Publishing date	2023-10-16
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2452967-9
ISSN	1662-5099
ISSN	1662-5099
DOI	10.3389/fnmol.2023.1274268
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Article ; Online: Poly(3-hydroxybutyrate-co-3-hydroxyhexanoate) Bionanocomposites with Crystalline Nanocellulose and Graphene Oxide: Experimental Results and Support Vector Machine Modeling.

Champa-Bujaico, Elizabeth / Díez-Pascual, Ana M / Garcia-Diaz, Pilar

Polymers

2023 Volume 15, Issue 18

Abstract: ... in the form of a support vector machine (SVM). The model performance was evaluated in terms of the mean ... for various concentrations of CNC and GO were accurately predicted using a machine learning (ML) model ... differential scanning calorimetry (DSC). The nanofillers had a nucleating role, raising the crystallization temperature ...

Abstract	Poly(3-hydroxybutyrate-co-3-hydroxyhexanoate) (PHBHHx) is a biodegradable and biocompatible bacterial copolymer used in the biomedical and food industries. However, it displays low stiffness and strength for certain applications. This issue can be solved via reinforcement with nanofillers. In this work, PHBHHx-based bionanocomposites reinforced with different loadings of crystalline nanocellulose (CNC) and graphene oxide (GO) were developed by a green and straightforward solution casting technique. Their crystalline nature and surface topography were explored via X-ray diffraction (XRD) and field-emission scanning electron microscopy (FE-SEM), respectively, their composition was corroborated via Fourier-transformed infrared spectroscopy (FTIR), and their crystallization and melting behavior were determined via differential scanning calorimetry (DSC). The nanofillers had a nucleating role, raising the crystallization temperature of the polymer, whilst hardly any changes were found in the melting temperature. Further, significant enhancements in the stiffness, strength, and thermal stability of the PHBHHx matrix were observed with the incorporation of both nanofillers, which was attributed to a synergic effect. The mechanical properties for various concentrations of CNC and GO were accurately predicted using a machine learning (ML) model in the form of a support vector machine (SVM). The model performance was evaluated in terms of the mean absolute error (MAE), the mean square error (MSE), and the correlation coefficient (
Language	English
Publishing date	2023-09-13
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2527146-5
ISSN	2073-4360 ; 2073-4360
ISSN (online)	2073-4360
ISSN	2073-4360
DOI	10.3390/polym15183746
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