Severity modeling of propionic acidemia using clinical and laboratory biomarkers

Abstract Purpose To conduct a proof-of-principle study to identify subtypes of propionic acidemia (PA) and associated biomarkers. Methods Data from a clinically diverse PA patient population (https://clinicaltrials.gov/ct2/show/NCT02890342) were used to train and test machine learning models, identify PA-relevant biomarkers, and perform validation analysis using data from liver-transplanted participants. k-Means clustering was used to test for the existence of PA subtypes. Expert knowledge was used to define PA subtypes (mild and severe). Given expert classification, supervised machine learning (support vector machine with a polynomial kernel, svmPoly) performed dimensional reduction to define relevant features of each PA subtype. Results Forty participants enrolled in the study; five underwent liver transplant. Analysis with k-means clustering indicated that several PA subtypes may exist on the biochemical continuum. The conventional PA biomarkers, plasma total 2-methylctirate and propionylcarnitine, were not statistically significantly different between nontransplanted and transplanted participants motivating us to search for other biomarkers. Unbiased dimensional reduction using svmPoly revealed that plasma transthyretin, alanine:serine ratio, GDF15, FGF21, and in vivo 1-13C-propionate oxidation, play roles in defining PA subtypes. Conclusion Support vector machine prioritized biomarkers that helped classify propionic acidemia patients according to severity subtypes, with important ramifications for future clinical trials and management of PA.

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Supervised Machine Learning Methods and Hyperspectral Imaging Techniques Jointly Applied for Brain Cancer Classification

Sensors ◽

10.3390/s21113827 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3827

Author(s):

Gemma Urbanos ◽

Alberto Martín ◽

Guillermo Vázquez ◽

Marta Villanueva ◽

Manuel Villa ◽

...

Keyword(s):

Machine Learning ◽

Blood Vessel ◽

Hyperspectral Imaging ◽

Imaging Techniques ◽

Venous Blood ◽

Healthy Tissue ◽

Supervised Machine Learning ◽

Support Vector ◽

Arterial Blood

Hyperspectral imaging techniques (HSI) do not require contact with patients and are non-ionizing as well as non-invasive. As a consequence, they have been extensively applied in the medical field. HSI is being combined with machine learning (ML) processes to obtain models to assist in diagnosis. In particular, the combination of these techniques has proven to be a reliable aid in the differentiation of healthy and tumor tissue during brain tumor surgery. ML algorithms such as support vector machine (SVM), random forest (RF) and convolutional neural networks (CNN) are used to make predictions and provide in-vivo visualizations that may assist neurosurgeons in being more precise, hence reducing damages to healthy tissue. In this work, thirteen in-vivo hyperspectral images from twelve different patients with high-grade gliomas (grade III and IV) have been selected to train SVM, RF and CNN classifiers. Five different classes have been defined during the experiments: healthy tissue, tumor, venous blood vessel, arterial blood vessel and dura mater. Overall accuracy (OACC) results vary from 60% to 95% depending on the training conditions. Finally, as far as the contribution of each band to the OACC is concerned, the results obtained in this work are 3.81 times greater than those reported in the literature.

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Classifying Lensed Gravitational Waves in the Geometrical Optics Limit with Machine Learning

American Journal of Undergraduate Research ◽

10.33697/ajur.2019.019 ◽

2019 ◽

Vol 16 (2) ◽

pp. 5-16

Author(s):

Amit Singh ◽

Ivan Li ◽

Otto Hannuksela ◽

Tjonnie Li ◽

Kyungmin Kim

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Gravitational Wave ◽

Gravitational Waves ◽

Geometrical Optics ◽

Supervised Machine Learning ◽

Support Vector ◽

Multi Layer Perceptron ◽

Machine Learning Classifiers ◽

Learning Classifiers

Gravitational waves are theorized to be gravitationally lensed when they propagate near massive objects. Such lensing effects cause potentially detectable repeated gravitational wave patterns in ground- and space-based gravitational wave detectors. These effects are difficult to discriminate when the lens is small and the repeated patterns superpose. Traditionally, matched filtering techniques are used to identify gravitational-wave signals, but we instead aim to utilize machine learning techniques to achieve this. In this work, we implement supervised machine learning classifiers (support vector machine, random forest, multi-layer perceptron) to discriminate such lensing patterns in gravitational wave data. We train classifiers with spectrograms of both lensed and unlensed waves using both point-mass and singular isothermal sphere lens models. As the result, classifiers return F1 scores ranging from 0:852 to 0:996, with precisions from 0:917 to 0:992 and recalls ranging from 0:796 to 1:000 depending on the type of classifier and lensing model used. This supports the idea that machine learning classifiers are able to correctly determine lensed gravitational wave signals. This also suggests that in the future, machine learning classifiers may be used as a possible alternative to identify lensed gravitational wave events and to allow us to study gravitational wave sources and massive astronomical objects through further analysis. KEYWORDS: Gravitational Waves; Gravitational Lensing; Geometrical Optics; Machine Learning; Classification; Support Vector Machine; Random Tree Forest; Multi-layer Perceptron

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Classifying relevant video tutorials for the school’s learning management system using support vector machine algorithm

10.31219/osf.io/scz4r ◽

2020 ◽

Author(s):

Castro Mayleen Dorcas Bondoc ◽

Tumibay Gilbert Malawit

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Learning Process ◽

Learning Algorithm ◽

Research Work ◽

Supervised Machine Learning ◽

Support Vector ◽

Video Tutorials ◽

Learning Management ◽

Face To Face Instruction

Today many schools, universities and institutions recognize the necessity and importance of using Learning Management Systems (LMS) as part of their educational services. This research work has applied LMS in the teaching and learning process of Bulacan State University (BulSU) Graduate School (GS) Program that enhances the face-to-face instruction with online components. The researchers uses an LMS that provides educators a platform that can motivate and engage students to new educational environment through manage online classes. The LMS allows educators to distribute information, manage learning materials, assignments, quizzes, and communications. Aside from the basic functions of the LMS, the researchers uses Machine Learning (ML) Algorithms applying Support Vector Machine (SVM) that will classify and identify the best related videos per topic. SVM is a supervised machine learning algorithm that analyzes data for classification and regression analysis by Maity [1]. The results of this study showed that integration of video tutorials in LMS can significantly contribute knowledge and skills in the learning process of the students.

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PREDICTIVE MODELLING AND ANALYTICS FOR DIABETES USING A MACHINE LEARNING APPROACH

INFORMATION TECHNOLOGY IN INDUSTRY ◽

10.17762/itii.v9i1.121 ◽

2021 ◽

Vol 9 (1) ◽

pp. 215-223

Author(s):

Prateek Mishra, Dr.Anurag Sharma, Dr. Abhishek Badholia

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Machine Learning Algorithms ◽

Computational Method ◽

Supervised Machine Learning ◽

Undiagnosed Diabetes ◽

Support Vector ◽

Entire Body ◽

Data Manipulation ◽

Kernel Support Vector Machine

Adverse effects can be seen in the entire body due to the major disorders known as Diabetes. The risk of dangers like diabetic nephropathy, cardiac stroke and other disorders can increase severally because of the undiagnosed diabetes. Around the globe the people are suffering from this disease. For a healthy life early detection of this disease is very curtail. As the causes of the diabetes is increasing rapidly this disease might turn up as a reason for worldwide concern. Increasing the chances for a more accurate predictions and form experiences automatic learning by computational method may be provided by Machine Learning (ML). With the help of R data manipulation tool for trends development and with risk factor patterns detection in Pima Indian diabetes technique of machine learning is been used in the current researches. With the use of R data manipulation tool analysis and development five different predictive models is done for the categorization of patients into diabetic and non- diabetic. supervised machine learning algorithms namely multifactor dimensionality reduction (MDR), k-nearest neighbor (k-NN), artificial neural network (ANN) radial basis function (RBF) kernel support vector machine and linear kernel support vector machine (SVM-linear) are used for this purpose.

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Evaluating Annotated Dataset of Customer Reviews for Aspect Based Sentiment Analysis

Journal of Web Engineering ◽

10.13052/jwe1540-9589.2122 ◽

2021 ◽

Author(s):

Dimple Chehal ◽

Parul Gupta ◽

Payal Gulati

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Sentiment Analysis ◽

Nearest Neighbor ◽

Supervised Machine Learning ◽

Support Vector ◽

Product Reviews ◽

K Nearest Neighbor ◽

Customer Reviews ◽

Percent Accuracy

Sentiment analysis of product reviews on e-commerce platforms aids in determining the preferences of customers. Aspect-based sentiment analysis (ABSA) assists in identifying the contributing aspects and their corresponding polarity, thereby allowing for a more detailed analysis of the customer’s inclination toward product aspects. This analysis helps in the transition from the traditional rating-based recommendation process to an improved aspect-based process. To automate ABSA, a labelled dataset is required to train a supervised machine learning model. As the availability of such dataset is limited due to the involvement of human efforts, an annotated dataset has been provided here for performing ABSA on customer reviews of mobile phones. The dataset comprising of product reviews of Apple-iPhone11 has been manually annotated with predefined aspect categories and aspect sentiments. The dataset’s accuracy has been validated using state-of-the-art machine learning techniques such as Naïve Bayes, Support Vector Machine, Logistic Regression, Random Forest, K-Nearest Neighbor and Multi Layer Perceptron, a sequential model built with Keras API. The MLP model built through Keras Sequential API for classifying review text into aspect categories produced the most accurate result with 67.45 percent accuracy. K- nearest neighbor performed the worst with only 49.92 percent accuracy. The Support Vector Machine had the highest accuracy for classifying review text into aspect sentiments with an accuracy of 79.46 percent. The model built with Keras API had the lowest 76.30 percent accuracy. The contribution is beneficial as a benchmark dataset for ABSA of mobile phone reviews.

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Support Vector Machine

Handbook of Research on Modern Systems Analysis and Design Technologies and Applications ◽

10.4018/978-1-59904-887-1.ch028 ◽

2009 ◽

pp. 501-522 ◽

Cited By ~ 1

Author(s):

A. B.M. Shawkat Ali

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

System Analysis ◽

Learning Algorithm ◽

Mathematical Formulation ◽

Supervised Machine Learning ◽

Future Research ◽

Support Vector ◽

Analysis And Design ◽

Simple Demonstration

From the beginning, machine learning methodology, which is the origin of artificial intelligence, has been rapidly spreading in the different research communities with successful outcomes. This chapter aims to introduce for system analysers and designers a comparatively new statistical supervised machine learning algorithm called support vector machine (SVM). We explain two useful areas of SVM, that is, classification and regression, with basic mathematical formulation and simple demonstration to make easy the understanding of SVM. Prospects and challenges of future research in this emerging area are also described. Future research of SVM will provide improved and quality access to the users. Therefore, developing an automated SVM system with state-of-the-art technologies is of paramount importance, and hence, this chapter will link up an important step in the system analysis and design perspective to this evolving research arena.

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Implementation of n-gram Methodology for Rotten Tomatoes Review Dataset Sentiment Analysis

International Journal of Knowledge Discovery in Bioinformatics ◽

10.4018/ijkdb.2017010103 ◽

2017 ◽

Vol 7 (1) ◽

pp. 30-41 ◽

Cited By ~ 12

Author(s):

Prayag Tiwari ◽

Brojo Kishore Mishra ◽

Sachin Kumar ◽

Vivek Kumar

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Sentiment Analysis ◽

Maximum Entropy ◽

Learning Strategies ◽

Supervised Machine Learning ◽

Support Vector ◽

N Gram ◽

F Measure ◽

Blog Posts

Sentiment Analysis intends to get the basic perspective of the content, which may be anything that holds a subjective supposition, for example, an online audit, Comments on Blog posts, film rating and so forth. These surveys and websites might be characterized into various extremity gatherings, for example, negative, positive, and unbiased keeping in mind the end goal to concentrate data from the info dataset. Supervised machine learning strategies group these reviews. In this paper, three distinctive machine learning calculations, for example, Support Vector Machine (SVM), Maximum Entropy (ME) and Naive Bayes (NB), have been considered for the arrangement of human conclusions. The exactness of various strategies is basically inspected keeping in mind the end goal to get to their execution on the premise of parameters, e.g. accuracy, review, f-measure, and precision.

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Encrypted DNP3 Traffic Classification Using Supervised Machine Learning Algorithms

Machine Learning and Knowledge Extraction ◽

10.3390/make1010022 ◽

2019 ◽

Vol 1 (1) ◽

pp. 384-399 ◽

Cited By ~ 2

Author(s):

Thais de Toledo ◽

Nunzio Torrisi

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Decision Tree ◽

Smart Grids ◽

Learning Algorithms ◽

Electric Utility ◽

Machine Learning Algorithms ◽

Supervised Machine Learning ◽

Support Vector ◽

Communication Link

The Distributed Network Protocol (DNP3) is predominately used by the electric utility industry and, consequently, in smart grids. The Peekaboo attack was created to compromise DNP3 traffic, in which a man-in-the-middle on a communication link can capture and drop selected encrypted DNP3 messages by using support vector machine learning algorithms. The communication networks of smart grids are a important part of their infrastructure, so it is of critical importance to keep this communication secure and reliable. The main contribution of this paper is to compare the use of machine learning techniques to classify messages of the same protocol exchanged in encrypted tunnels. The study considers four simulated cases of encrypted DNP3 traffic scenarios and four different supervised machine learning algorithms: Decision tree, nearest-neighbor, support vector machine, and naive Bayes. The results obtained show that it is possible to extend a Peekaboo attack over multiple substations, using a decision tree learning algorithm, and to gather significant information from a system that communicates using encrypted DNP3 traffic.

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Pancreatic Cancer Early Detection Using Twin Support Vector Machine Based on Kernel

Symmetry ◽

10.3390/sym12040667 ◽

2020 ◽

Vol 12 (4) ◽

pp. 667

Author(s):

Wismaji Sadewo ◽

Zuherman Rustam ◽

Hamidah Hamidah ◽

Alifah Roudhoh Chusmarsyah

Keyword(s):

Machine Learning ◽

Pancreatic Cancer ◽

Support Vector Machine ◽

Early Detection ◽

Feature Space ◽

Kernel Functions ◽

Twin Support Vector Machine ◽

Polynomial Kernel ◽

Support Vector ◽

Rbf Kernel

Early detection of pancreatic cancer is difficult, and thus many cases of pancreatic cancer are diagnosed late. When pancreatic cancer is detected, the cancer is usually well developed. Machine learning is an approach that is part of artificial intelligence and can detect pancreatic cancer early. This paper proposes a machine learning approach with the twin support vector machine (TWSVM) method as a new approach to detecting pancreatic cancer early. TWSVM aims to find two symmetry planes such that each plane has a distance close to one data class and as far as possible from another data class. TWSVM is fast in building a model and has good generalizations. However, TWSVM requires kernel functions to operate in the feature space. The kernel functions commonly used are the linear kernel, polynomial kernel, and radial basis function (RBF) kernel. This paper uses the TWSVM method with these kernels and compares the best kernel for use by TWSVM to detect pancreatic cancer early. In this paper, the TWSVM model with each kernel is evaluated using a 10-fold cross validation. The results obtained are that TWSVM based on the kernel is able to detect pancreatic cancer with good performance. However, the best kernel obtained is the RBF kernel, which produces an accuracy of 98%, a sensitivity of 97%, a specificity of 100%, and a running time of around 1.3408 s.

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Comparing Supervised Machine Learning Strategies and Linguistic Features to Search for Very Negative Opinions

Information ◽

10.3390/info10010016 ◽

2019 ◽

Vol 10 (1) ◽

pp. 16 ◽

Cited By ~ 3

Author(s):

Sattam Almatarneh ◽

Pablo Gamallo

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Empirical Study ◽

Learning Strategies ◽

Supervised Machine Learning ◽

Support Vector ◽

Word Embeddings ◽

Linguistic Features ◽

Machine Learning Classifiers ◽

Supervised Machine Learning Classifiers

In this paper, we examine the performance of several classifiers in the process of searching for very negative opinions. More precisely, we do an empirical study that analyzes the influence of three types of linguistic features (n-grams, word embeddings, and polarity lexicons) and their combinations when they are used to feed different supervised machine learning classifiers: Naive Bayes (NB), Decision Tree (DT), and Support Vector Machine (SVM). The experiments we have carried out show that SVM clearly outperforms NB and DT in all datasets by taking into account all features individually as well as their combinations.

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