scholarly journals Capacity Random Forest for Correlative Multiple Criteria Decision Pattern Learning

Mathematics ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 1372
Author(s):  
Jian-Zhang Wu ◽  
Feng-Feng Chen ◽  
Yan-Qing Li ◽  
Li Huang
Keyword(s):  
Random Forest ◽  
Decision Trees ◽  
Multiple Criteria ◽  
Learning Ability ◽  
Pattern Learning ◽  
Multiple Sources ◽  
Choquet Capacity ◽  
Fitting Method ◽  
Sorting Problems ◽  
The One

The Choquet capacity and integral is an eminent scheme to represent the interaction knowledge among multiple decision criteria and deal with the independent multiple sources preference information. In this paper, we enhance this scheme’s decision pattern learning ability by combining it with another powerful machine learning tool, the random forest of decision trees. We first use the capacity fitting method to train the Choquet capacity and integral-based decision trees and then compose them into the capacity random forest (CRF) to better learn and explain the given decision pattern. The CRF algorithms of solving the correlative multiple criteria based ranking and sorting decision problems are both constructed and discussed. Two illustrative examples are given to show the feasibilities of the proposed algorithms. It is shown that on the one hand, CRF method can provide more detailed explanation information and a more reliable collective prediction result than the main existing capacity fitting methods; on the other hand, CRF extends the applicability of the traditional random forest method into solving the multiple criteria ranking and sorting problems with a relatively small pool of decision learning data.

scholarly journals Challenges for a QCD axion at the 10 MeV scale

2021 ◽  
Vol 2021 (5) ◽  
Author(s):  
Jia Liu ◽  
Navin McGinnis ◽  
Carlos E. M. Wagner ◽  
Xiao-Ping Wang
Keyword(s):  
Higgs Boson ◽  
Standard Model ◽  
Structure Constant ◽  
Fine Structure Constant ◽  
Multiple Sources ◽  
Standard Model Extension ◽  
Mixing Angles ◽  
Quark Masses ◽  
Model Extension ◽  
The One

Abstract We report on an interesting realization of the QCD axion, with mass in the range $$ \mathcal{O} $$ O (10) MeV. It has previously been shown that although this scenario is stringently constrained from multiple sources, the model remains viable for a range of parameters that leads to an explanation of the Atomki experiment anomaly. In this article we study in more detail the additional constraints proceeding from recent low energy experiments and study the compatibility of the allowed parameter space with the one leading to consistency of the most recent measurements of the electron anomalous magnetic moment and the fine structure constant. We further provide an ultraviolet completion of this axion variant and show the conditions under which it may lead to the observed quark masses and CKM mixing angles, and remain consistent with experimental constraints on the extended scalar sector appearing in this Standard Model extension. In particular, the decay of the Standard Model-like Higgs boson into two light axions may be relevant and leads to a novel Higgs boson signature that may be searched for at the LHC in the near future.

scholarly journals Prediction of Heart Disease Using Machine Learning Algorithms

2018 ◽  
Vol 7 (2.32) ◽  
pp. 363 ◽  
Author(s):  
N Rajesh ◽  
Maneesha T ◽  
Shaik Hafeez ◽  
Hari Krishna
Keyword(s):  
Risk Factors ◽  
Heart Disease ◽  
Decision Trees ◽  
Naive Bayes ◽  
Heart Diseases ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Common Disease ◽  
Bayes Algorithm ◽  
The One

Heart disease is the one of the most common disease. This disease is quite common now a days we used different attributes which can relate to this heart diseases well to find the better method to predict and we also used algorithms for prediction. Naive Bayes, algorithm is analyzed on dataset based on risk factors. We also used decision trees and combination of algorithms for the prediction of heart disease based on the above attributes. The results shown that when the dataset is small naive Bayes algorithm gives the accurate results and when the dataset is large decision trees gives the accurate results.  

scholarly journals Fault diagnosis method of submersible screw pump based on random forest

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0242458
Author(s):  
Minzheng Jiang ◽  
Tiancai Cheng ◽  
Kangxing Dong ◽  
Shufan Xu ◽  
Yulong Geng
Keyword(s):  
Fault Diagnosis ◽  
Random Forest ◽  
Decision Trees ◽  
Processing System ◽  
Random Forest Model ◽  
Categorical Variables ◽  
Oil Well ◽  
Screw Pump ◽  
Forest Model ◽  
Diagnosis Method

The difficulty in directly determining the failure mode of the submersible screw pump will shorten the life of the system and the normal production of the oil well. This thesis aims to identify the fault forms of submersible screw pump accurately and efficiently, and proposes a fault diagnosis method of the submersible screw pump based on random forest. HDFS storage system and MapReduce processing system are established based on Hadoop big data processing platform; Furthermore, the Bagging algorithm is used to collect the training set data. Also, this thesis adopts the CART method to establish the sample library and the decision trees for a random forest model. Six continuous variables, four categorical variables and fault categories of submersible screw pump oil production system are used for training the decision trees. As several decision trees constitute a random forest model, the parameters to be tested are input into the random forest models, and various types of decision trees are used to determine the failure category in the submersible screw pump. It has been verified that the accuracy rate of fault diagnosis is 92.86%. This thesis can provide some meaningful guidance for timely detection of the causes of downhole unit failures, reducing oil well production losses, and accelerating the promotion and application of submersible screw pumps in oil fields.

scholarly journals Development of an ensemble machine learning prognostic model to predict 60-day risk of major adverse cardiac events in adults with chest pain

2021 ◽  
Author(s):  
Chris J. Kennedy ◽  
Dustin G. Mark ◽  
Jie Huang ◽  
Mark J. van der Laan ◽  
Alan E. Hubbard ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Chest Pain ◽  
Random Forest ◽  
Decision Trees ◽  
Low Risk ◽  
Major Adverse Cardiac Events ◽  
Risk Scores ◽  
Cardiac Events ◽  
Adverse Cardiac Events

Background: Chest pain is the second leading reason for emergency department (ED) visits and is commonly identified as a leading driver of low-value health care. Accurate identification of patients at low risk of major adverse cardiac events (MACE) is important to improve resource allocation and reduce over-treatment. Objectives: We sought to assess machine learning (ML) methods and electronic health record (EHR) covariate collection for MACE prediction. We aimed to maximize the pool of low-risk patients that are accurately predicted to have less than 0.5% MACE risk and may be eligible for reduced testing. Population Studied: 116,764 adult patients presenting with chest pain in the ED and evaluated for potential acute coronary syndrome (ACS). 60-day MACE rate was 1.9%. Methods: We evaluated ML algorithms (lasso, splines, random forest, extreme gradient boosting, Bayesian additive regression trees) and SuperLearner stacked ensembling. We tuned ML hyperparameters through nested ensembling, and imputed missing values with generalized low-rank models (GLRM). We benchmarked performance to key biomarkers, validated clinical risk scores, decision trees, and logistic regression. We explained the models through variable importance ranking and accumulated local effect visualization. Results: The best discrimination (area under the precision-recall [PR-AUC] and receiver operating characteristic [ROC-AUC] curves) was provided by SuperLearner ensembling (0.148, 0.867), followed by random forest (0.146, 0.862). Logistic regression (0.120, 0.842) and decision trees (0.094, 0.805) exhibited worse discrimination, as did risk scores [HEART (0.064, 0.765), EDACS (0.046, 0.733)] and biomarkers [serum troponin level (0.064, 0.708), electrocardiography (0.047, 0.686)]. The ensemble's risk estimates were miscalibrated by 0.2 percentage points. The ensemble accurately identified 50% of patients to be below a 0.5% 60-day MACE risk threshold. The most important predictors were age, peak troponin, HEART score, EDACS score, and electrocardiogram. GLRM imputation achieved 90% reduction in root mean-squared error compared to median-mode imputation. Conclusion: Use of ML algorithms, combined with broad predictor sets, improved MACE risk prediction compared to simpler alternatives, while providing calibrated predictions and interpretability. Standard risk scores may neglect important health information available in other characteristics and combined in nuanced ways via ML.

Overview Statement on Medical Records

PEDIATRICS ◽  
1975 ◽  
Vol 56 (2) ◽  
pp. 329-329 ◽  
Author(s):  
Hugh C. Thompson ◽  
Stanton J. Barron ◽  
John P. Connelly ◽  
Andrew Margileth ◽  
Richard Olmsted ◽  
...  
Keyword(s):  
Medical Record ◽  
Medical Records ◽  
Medical Information ◽  
Third Party ◽  
Specific Information ◽  
Multiple Sources ◽  
Chart Audit ◽  
Working Together ◽  
The One ◽  
The Individual

Historically, medical records have been maintamed by individual physicians to record specific information concerning patients. This information was often understandable only to the writer. The data were of outstanding events. This was thought to be sufficient documentation for patient care. Records are now read by others than the individual physicians. Groups of physicians working together often share the same patients and their records. Patients may have multiple sources of care. Our population has become more mobile which makes it necessary to transfer vast amounts of medical information. The medical record many times is the one instrument which gives a complete and continuous documentation of the patient's medical history. Third-party payers are requesting access to medical records to document services provided. Chart audit is being tested as a mechanism for evaluating physician performance. Records must reflect what the physician does in order to be useful in such an appraisal. Much clinical research on the delivery of health care depends on accurately kept records which are easily interpreted. A chart is also a legal document for the protection of the physician as well as the patient. Thus, records will be used in other than traditional ways. Proper confidentiality must be maintained when such uses are necessary. Physicians generally agree as to the essential content of a medical record. However, there is little unanimity as to the structure of the chart. No one system of keeping records is now appropriate for all situations. The maintenance of adequate charts requires additional cost in both time and money.

Abstract 14955: Machine Learning Predicts Hemodynamic Instability in Children After Cardiac Surgery in Pediatric Intensive Care Unit (PICU)

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Koichi Sughimoto ◽  
Jacob Levman ◽  
Fazleem Baig ◽  
Derek Berger ◽  
Yoshihiro Oshima ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cardiac Surgery ◽  
Random Forest ◽  
Decision Trees ◽  
Blood Lactate ◽  
Heart Surgery ◽  
Ground Truth ◽  
Hemodynamic Instability ◽  
Serum Lactate ◽  
Lactate Levels

Introduction: Despite improvements in management for children after cardiac surgery, a non-negligible proportion of patients suffer from cardiac arrest, having a poor prognosis. Although serum lactate levels are widely accepted markers of hemodynamic instability, measuring lactate requires discrete blood sampling. An alternative method to evaluate hemodynamic stability/instability continuously and non-invasively may assist in improving the standard of patient care. Hypothesis: We hypothesize that blood lactate in PICU patients can be predicted using machine learning applied to arterial waveforms and perioperative characteristics. Methods: Forty-eight children, who underwent heart surgery, were included. Patient characteristics and physiological measurements were acquired and analyzed using specialized software/hardware, including heart rate, lactate level, arterial waveform sharpness, and area under the curve. Predicting a patient’s blood lactate levels was accomplished using regression-based supervised learning algorithms, including regression decision trees, tuned decision trees, random forest regressor, tuned random forest, AdaBoost regressor, and hypertuned AdaBoost. All algorithms were compared with hold-out cross validation. Two approaches were considered: basing prediction on the currently acquired physiological measurements along with those acquired at admission, as well as adding the most recent lactate measurement and the time since that measurement as prediction parameters. The second approach supports updating the learning system’s predictive capacity whenever a patient has a new ground truth blood lactate reading acquired. Results: In both approaches, the best performing machine learning method was the tuned random forest, which yielded a mean absolute error of 5.60 mg/dL in the first approach, and 4.62 mg/dL when predicting blood lactate with updated ground truth. Conclusions: In conclusion, the tuned random forest is capable of predicting the level of serum lactate by analyzing perioperative variables, including the arterial pressure waveform. Machine learning can predict the patient’s hemodynamics non-invasively, continuously, and with accuracy that may demonstrate clinical utility.

Detecting Cognitive Distraction Using Random Forest by Considering Eye Movement Type

2018 ◽  
pp. 1587-1599
Author(s):  
Hiroaki Koma ◽  
Taku Harada ◽  
Akira Yoshizawa ◽  
Hirotoshi Iwasaki
Keyword(s):  
Machine Learning ◽  
Eye Movements ◽  
Random Forest ◽  
Decision Trees ◽  
Eye Movement ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Still Images ◽  
Cognitive Distraction ◽  
Movement Type

Detecting distracted states can be applied to various problems such as danger prevention when driving a car. A cognitive distracted state is one example of a distracted state. It is known that eye movements express cognitive distraction. Eye movements can be classified into several types. In this paper, the authors detect a cognitive distraction using classified eye movement types when applying the Random Forest machine learning algorithm, which uses decision trees. They show the effectiveness of considering eye movement types for detecting cognitive distraction when applying Random Forest. The authors use visual experiments with still images for the detection.

DECISION TREES Y RANDOM FOREST MEDIANTE HERRAMIENTAS OPEN SOURCE:

2021 ◽  
pp. 91-104
Author(s):  
Guadalupe Peláez Ramírez ◽  
Francisco Javier Lena-Acebo
Keyword(s):  
Random Forest ◽  
Open Source ◽  
Decision Trees

One Parameter Chi-squared Analysis

2019 ◽  
pp. 18-31
Author(s):  
Carey Witkov ◽  
Keith Zengel
Keyword(s):  
Curve Fitting ◽  
Physical Experiments ◽  
Fitting Method ◽  
Wide Range ◽  
Chi Squared ◽  
Measurement Uncertainties ◽  
Distributed Measurement ◽  
Curve Fitting Method ◽  
The One ◽  
Parameter Case

The chi-squared method for parameter estimation and model testing is developed for the one-parameter case of a line with a slope but no intercept. Curve fitting is motivated, and several methods for curve fitting are introduced. The chi-squared method is shown to be the optimal curve fitting method whenever Gaussian distributed measurement uncertainties and a model are present. The central limit theorem, which assures Gaussian distributed measurement uncertainties for a wide range of physical experiments, is introduced. End-of-chapter problems are included (with solutions in an appendix).

scholarly journals Robotic grasp detection based on image processing and random forest

2019 ◽  
Vol 79 (3-4) ◽  
pp. 2427-2446 ◽  
Author(s):  
Jiahao Zhang ◽  
Miao Li ◽  
Ying Feng ◽  
Chenguang Yang
Keyword(s):  
Image Processing ◽  
Random Forest ◽  
Main Idea ◽  
Morphological Image Processing ◽  
Segmentation Approach ◽  
Novel Objects ◽  
Set Up ◽  
The One ◽  
Objects Detection ◽  
Morphological Image

AbstractReal-time grasp detection plays a key role in manipulation, and it is also a complex task, especially for detecting how to grasp novel objects. This paper proposes a very quick and accurate approach to detect robotic grasps. The main idea is to perform grasping of novel objects in a typical RGB-D scene view. Our goal is not to find the best grasp for every object but to obtain the local optimal grasps in candidate grasp rectangles. There are three main contributions to our detection work. Firstly, an improved graph segmentation approach is used to do objects detection and it can separate objects from the background directly and fast. Secondly, we develop a morphological image processing method to generate candidate grasp rectangles set which avoids us to search grasp rectangles globally. Finally, we train a random forest model to predict grasps and achieve an accuracy of 94.26%. The model is mainly used to score every element in our candidate grasps set and the one gets the highest score will be converted to the final grasp configuration for robots. For real-world experiments, we set up our system on a tabletop scene with multiple objects and when implementing robotic grasps, we control Baxter robot with a different inverse kinematics strategy rather than the built-in one.

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