scholarly journals Expert-enhanced machine learning for cardiac arrhythmia classification

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0261571
Author(s):  
Sebastian Sager ◽  
Felix Bernhardt ◽  
Florian Kehrle ◽  
Maximilian Merkert ◽  
Andreas Potschka ◽  
...  
Keyword(s):  
Machine Learning ◽  
Atrial Flutter ◽  
Cardiac Arrhythmia ◽  
Domain Knowledge ◽  
Expert Knowledge ◽  
Mathematical Optimization ◽  
Regression Problem ◽  
Additional Advantage ◽  
Inverse Simulation

We propose a new method for the classification task of distinguishing atrial fibrillation (AFib) from regular atrial tachycardias including atrial flutter (AFlu) based on a surface electrocardiogram (ECG). Recently, many approaches for an automatic classification of cardiac arrhythmia were proposed and to our knowledge none of them can distinguish between these two. We discuss reasons why deep learning may not yield satisfactory results for this task. We generate new and clinically interpretable features using mathematical optimization for subsequent use within a machine learning (ML) model. These features are generated from the same input data by solving an additional regression problem with complicated combinatorial substructures. The resultant can be seen as a novel machine learning model that incorporates expert knowledge on the pathophysiology of atrial flutter. Our approach achieves an unprecedented accuracy of 82.84% and an area under the receiver operating characteristic (ROC) curve of 0.9, which classifies as “excellent” according to the classification indicator of diagnostic tests. One additional advantage of our approach is the inherent interpretability of the classification results. Our features give insight into a possibly occurring multilevel atrioventricular blocking mechanism, which may improve treatment decisions beyond the classification itself. Our research ideally complements existing textbook cardiac arrhythmia classification methods, which cannot provide a classification for the important case of AFib↔AFlu. The main contribution is the successful use of a novel mathematical model for multilevel atrioventricular block and optimization-driven inverse simulation to enhance machine learning for classification of the arguably most difficult cases in cardiac arrhythmia. A tailored Branch-and-Bound algorithm was implemented for the domain knowledge part, while standard algorithms such as Adam could be used for training.

Programming Language Support for Implementing Machine Learning Algorithms

2020 ◽  
pp. 402-421
Author(s):  
Anitha Elavarasi S. ◽  
Jayanthi J.
Keyword(s):  
Machine Learning ◽  
Programming Languages ◽  
Programming Language ◽  
Domain Knowledge ◽  
Learning Algorithm ◽  
Mathematical Optimization ◽  
Machine Learning Algorithms ◽  
Application Development ◽  
Machine Learning Applications ◽  
And Control

Machine learning provides the system to automatically learn without human intervention and improve their performance with the help of previous experience. It can access the data and use it for learning by itself. Even though many algorithms are developed to solve machine learning issues, it is difficult to handle all kinds of inputs data in-order to arrive at accurate decisions. The domain knowledge of statistical science, probability, logic, mathematical optimization, reinforcement learning, and control theory plays a major role in developing machine learning based algorithms. The key consideration in selecting a suitable programming language for implementing machine learning algorithm includes performance, concurrence, application development, learning curve. This chapter deals with few of the top programming languages used for developing machine learning applications. They are Python, R, and Java. Top three programming languages preferred by data scientist are (1) Python more than 57%, (2) R more than 31%, and (3) Java used by 17% of the data scientists.

scholarly journals OPTIMAL POSITIONING METHODS OF INTEGRAL DEFORMATION SENSORS – EXPERT KNOWLEDGE VERSUS MATHEMATICAL OPTIMIZATION

2021 ◽  
Vol 2021 (3) ◽  
pp. 4628-4635
Author(s):  
Ch. Brecher ◽  
◽  
R. Herzog ◽  
A. Naumann ◽  
R. Spierling ◽  
...  
Keyword(s):  
Machine Tool ◽  
Domain Knowledge ◽  
Expert Knowledge ◽  
Kinematic Model ◽  
Mathematical Optimization ◽  
Elastic Behavior ◽  
Work Piece ◽  
Mathematical Methods ◽  
Optimal Position ◽  
Structural Deformations

Up to 75 % of the overall work piece error can be caused by the thermo-elastic behavior of the machine tool. Therefore, correction methods based on machine-integrated sensors were intensively researched during the last years, in order to determine the error of the Tool Center Point (TCP) parallel to the process. One of these methods includes the integral deformation sensor (IDS), which detects the deformation along the length of a structural component of the machine. The error of the TCP is modelled based on the measured structural deformations, a mechanical model of the structural parts and a kinematic model of the machine tool. Currently, the sensor setup for specific machines is usually defined by an expert with the help of his or her domain knowledge. There are existing mathematical methods for optimal sensor positioning. The aim of this work is the evaluation of the expert positioning versus the mathematical methods. The parameters to be varied are the lengths and positions of the IDS. Criteria for the evaluation are the achievable accuracy of the TCP error prediction and the sensitivity to small variations of the optimal position, as they might occur during the installation.

Machine Learning Strategies for Accurate Log Prediction in Reservoir Characterization: Self-Calibrating Versus Domain-Knowledge

2021 ◽  
Author(s):  
Ahmed Reda Ali ◽  
Makky Sandra Jaya ◽  
Ernest A. Jones
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Prediction Accuracy ◽  
Domain Knowledge ◽  
Reservoir Characterization ◽  
Regression Problem ◽  
Core Data ◽  
Knowledge Based ◽  
Versus Domain ◽  
Porosity And Permeability

Abstract Petrophysical evaluation is a crucial task for reservoir characterization but it is often complicated, time-consuming and associated with uncertainties. Moreover, this job is subjective and ambiguous depending on the petrophysicist's experience. Utilizing the flourishing Artificial Intelligence (AI)/Machine Learning (ML) is a way to build an automating process with minimal human intervention, improving consistency and efficiency of well log prediction and interpretation. Nowadays, the argument is whether AI-ML should base on a statistically self-calibrating or knowledge-based prediction framework! In this study, we develop a petrophysically knowledge-based AI-ML workflow that upscale sparsely-sampled core porosity and permeability into continuous curves along the entire well interval. AI-ML focuses on making predictions from analyzing data by learning and identifying patterns. The accuracy of the self-calibrating statistical models is heavily dependent on the volume of training data. The proposed AI-ML workflow uses raw well logs (gamma-ray, neutron and density) to predict porosity and permeability over the well interval using sparsely core data. The challenge in building the AI-ML model is the number of data points used for training showed an imbalance in the relative sampling of plugs, i.e. the number of core data (used as target variable) is less than 10%. Ensemble learning and stacking ML approaches are used to obtain maximum predictive performance of self-calibrating learning strategy. Alternatively, a new petrophysical workflow is established to debrief the domain experience in the feature selection that is used as an important weight in the regression problem. This helps ML model to learn more accurately by discovering hidden relationships between independent and target variables. This workflow is the inference engine of the AI-ML model to extract relevant domain-knowledge within the system that leads to more accurate predictions. The proposed knowledge-driven ML strategy achieved a prediction accuracy of R2 score = 87% (Correlation Coefficient (CC) of 96%). This is a significant improvement by R2 = 57% (CC = 62%) compared to the best performing self-calibrating ML models. The predicted properties are upscaled automatically to predict uncored intervals, improving data coverage and property population in reservoir models leading to the improvement of the model robustness. The high prediction accuracy demonstrates the potential of knowledge-driven AI-ML strategy in predicting rock properties under data sparsity and limitations and saving significant cost and time. This paper describes an AI-ML workflow that predicts high-resolution continuous porosity and permeability logs from imbalanced and sparse core plug data. The method successfully incorporates new type petrophysical facies weight as a feature augmentation engine for ML domain-knowledge framework. The workflow consisted of petrophysical treatment of raw data includes log quality control, preconditioning, processing, features augmentation and labelling, followed by feature selection to impersonate domain experience.

Regression and Classification Method Based on Gaussian Processes

2014 ◽  
Vol 971-973 ◽  
pp. 1949-1952
Author(s):  
Xing Hui Wu ◽  
Yu Ping Zhou
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Gaussian Processes ◽  
Classification Problem ◽  
Support Vector ◽  
Machine Learning Method ◽  
Learning Method ◽  
Regression Problem ◽  
Development Direction

Gaussian processes is a kind of machine learning method developed in recent years and also a promising technology that has been applied both in the regression problem and the classification problem. In this paper, the general principle of regression and classification based on Gaussian process and experimental verification was described. A comparison about accuracy between this method and Support Vector Machine (SVM) is made during the experiments.Finally, it was summarized of the regression and classification of Gaussian process application and future development direction.

scholarly journals Analysis of IPL Match Results using Machine Learning Algorithms

2021 ◽  
Vol 9 (VI) ◽  
pp. 1746-1751
Author(s):  
N. Lokeswari
Keyword(s):  
Machine Learning ◽  
Classification Problem ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Large Set ◽  
Regression Problem ◽  
The Past ◽  
Premier League ◽  
Past Data

Indian Premier League (IPL) is a famous Twenty-20 League conducted by The Board of Control for Cricket in India (BCCI). It was started in 2008 and successfully completed its thirteen seasons till 2020. IPL is a popular sport where it has a large set of audience throughout the country. Every cricket fan would be eager to know and predict the IPL match results.A solution using Machine Learning is provided for the analysis of IPL Match results. This paper attempts to predict the match winner and the innings score considering the past data of match by match and ball by ball. Match winner prediction is taken as classification problem and innings score prediction is taken as regression problem. Algorithms like Support Vector Machine(SVM),Naive Bayes, k-Nearest Neighbour(kNN) are used for classification of match winner and Linear Regression, Decision tree for prediction of innings score. The dataset contains many features in which 7 features are identified in which that can be used for the prediction. Based on those features, models are built and evaluated by certain parameters. Based on the results SVM performed.

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