Robust well-log based determination of rock thermal conductivity through machine learning

2020 ◽  
Vol 222 (2) ◽  
pp. 978-988
Author(s):  
Yury Meshalkin ◽  
Anuar Shakirov ◽  
Evgeniy Popov ◽  
Dmitry Koroteev ◽  
Irina Gurbatova
Keyword(s):  
Machine Learning ◽  
Thermal Conductivity ◽  
Random Forest ◽  
Well Logging ◽  
Machine Learning Algorithms ◽  
System Modelling ◽  
Well Log ◽  
Random Forest Algorithm ◽  
Rock Thermal Conductivity

SUMMARY Rock thermal conductivity is an essential input parameter for enhanced oil recovery methods design and optimization and for basin and petroleum system modelling. Absence of any effective technique for direct in situ measurements of rock thermal conductivity makes the development of well-log based methods for rock thermal conductivity determination highly desirable. A major part of the existing problem solutions is regression model-based approaches. Literature review revealed that there are only several studies performed to assess the applicability of neural network-based algorithms to predict rock thermal conductivity from well-logging data. In this research, we aim to define the most effective machine-learning algorithms for well-log based determination of rock thermal conductivity. Well-logging data acquired at a heavy oil reservoir together with results of thermal logging on cores extracted from two wells were the basis for our research. Eight different regression models were developed and tested to predict vertical variations of rock conductivity from well-logging data. Additionally, rock thermal conductivity was determined based on Lichtenecker–Asaad model. Comparison study of regression-based and theoretical-based approaches was performed. Among considered machine learning techniques Random Forest algorithm was found to be the most accurate at well-log based determination of rock thermal conductivity. From a comparison of the thermal conductivity—depth profile predicted from well-logging data with the experimental data, and it can be concluded that thermal conductivity can be determined with a total relative error of 12.54 per cent. The obtained results prove that rock thermal conductivity can be inferred from well-logging data for wells that are drilled in a similar geological setting based on the Random Forest algorithm with an accuracy sufficient for industrial needs.

scholarly journals Random Forest Algorithm to Investigate the Case of Acute Coronary Syndrome

2021 ◽  
Vol 5 (2) ◽  
pp. 369-378
Author(s):  
Eka Pandu Cynthia ◽  
M. Afif Rizky A. ◽  
Alwis Nazir ◽  
Fadhilah Syafria
Keyword(s):  
Machine Learning ◽  
Acute Coronary Syndrome ◽  
Random Forest ◽  
Data Science ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Random Forest Algorithm ◽  
Coronary Syndrome ◽  
Use Of Data

This paper explains the use of the Random Forest Algorithm to investigate the Case of Acute Coronary Syndrome (ACS). The objectives of this study are to review the evaluation of the use of data science techniques and machine learning algorithms in creating a model that can classify whether or not cases of acute coronary syndrome occur. The research method used in this study refers to the IBM Foundational Methodology for Data Science, include: i) inventorying dataset about ACS, ii) preprocessing for the data into four sub-processes, i.e. requirements, collection, understanding, and preparation, iii) determination of RFA, i.e. the "n" of the tree which will form a forest and forming trees from the random forest that has been created, and iv) determination of the model evaluation and result in analysis based on Python programming language. Based on the experiments that the learning have been conducted using a random forest machine-learning algorithm with an n-estimator value of 100 and each tree's depth (max depth) with a value of 4, learning scenarios of 70:30, 80:20, and 90:10 on 444 cases of acute coronary syndrome data. The results show that the 70:30 scenario model has the best results, with an accuracy value of 83.45%, a precision value of 85%, and a recall value of 92.4%. Conclusions obtained from the experiment results were evaluated with various statistical metrics (accuracy, precision, and recall) in each learning scenario on 444 cases of acute coronary syndrome data with a cross-validation value of 10 fold.

scholarly journals Prediction of novel mouse TLR9 agonists using a random forest approach

2019 ◽  
Vol 20 (S2) ◽  
Author(s):  
Varun Khanna ◽  
Lei Li ◽  
Johnson Fung ◽  
Shoba Ranganathan ◽  
Nikolai Petrovsky
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Correlation Coefficient ◽  
Matthews Correlation Coefficient ◽  
Learning Algorithms ◽  
Ensemble Classifier ◽  
Innate Immune ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Random Forest Algorithm

Abstract Background Toll-like receptor 9 is a key innate immune receptor involved in detecting infectious diseases and cancer. TLR9 activates the innate immune system following the recognition of single-stranded DNA oligonucleotides (ODN) containing unmethylated cytosine-guanine (CpG) motifs. Due to the considerable number of rotatable bonds in ODNs, high-throughput in silico screening for potential TLR9 activity via traditional structure-based virtual screening approaches of CpG ODNs is challenging. In the current study, we present a machine learning based method for predicting novel mouse TLR9 (mTLR9) agonists based on features including count and position of motifs, the distance between the motifs and graphically derived features such as the radius of gyration and moment of Inertia. We employed an in-house experimentally validated dataset of 396 single-stranded synthetic ODNs, to compare the results of five machine learning algorithms. Since the dataset was highly imbalanced, we used an ensemble learning approach based on repeated random down-sampling. Results Using in-house experimental TLR9 activity data we found that random forest algorithm outperformed other algorithms for our dataset for TLR9 activity prediction. Therefore, we developed a cross-validated ensemble classifier of 20 random forest models. The average Matthews correlation coefficient and balanced accuracy of our ensemble classifier in test samples was 0.61 and 80.0%, respectively, with the maximum balanced accuracy and Matthews correlation coefficient of 87.0% and 0.75, respectively. We confirmed common sequence motifs including ‘CC’, ‘GG’,‘AG’, ‘CCCG’ and ‘CGGC’ were overrepresented in mTLR9 agonists. Predictions on 6000 randomly generated ODNs were ranked and the top 100 ODNs were synthesized and experimentally tested for activity in a mTLR9 reporter cell assay, with 91 of the 100 selected ODNs showing high activity, confirming the accuracy of the model in predicting mTLR9 activity. Conclusion We combined repeated random down-sampling with random forest to overcome the class imbalance problem and achieved promising results. Overall, we showed that the random forest algorithm outperformed other machine learning algorithms including support vector machines, shrinkage discriminant analysis, gradient boosting machine and neural networks. Due to its predictive performance and simplicity, the random forest technique is a useful method for prediction of mTLR9 ODN agonists.

scholarly journals A Machine Learning-Based Prediction Model for Cardiovascular Risk in Women With Preeclampsia

2021 ◽  
Vol 8 ◽  
Author(s):  
Guan Wang ◽  
Yanbo Zhang ◽  
Sijin Li ◽  
Jun Zhang ◽  
Dongkui Jiang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cardiovascular Disease ◽  
Cardiovascular Risk ◽  
Random Forest ◽  
Prediction Model ◽  
Disease Risk ◽  
Cardiovascular Disease Risk ◽  
Machine Learning Algorithms ◽  
Brier Score ◽  
Random Forest Algorithm

Objective: Preeclampsia affects 2–8% of women and doubles the risk of cardiovascular disease in women after preeclampsia. This study aimed to develop a model based on machine learning to predict postpartum cardiovascular risk in preeclamptic women.Methods: Collecting demographic characteristics and clinical serum markers associated with preeclampsia during pregnancy of 907 preeclamptic women retrospectively, we predicted the cardiovascular risk (ischemic heart disease, ischemic cerebrovascular disease, peripheral vascular disease, chronic kidney disease, metabolic system disease or arterial hypertension). The study samples were divided into training sets and test sets randomly in the ratio of 8:2. The prediction model was developed by 5 different machine learning algorithms, including Random Forest. 10-fold cross-validation was performed on the training set, and the performance of the model was evaluated on the test set.Results: Cardiovascular disease risk occurred in 186 (20.5%) of these women. By weighing area under the curve (AUC), the Random Forest algorithm presented the best performance (AUC = 0.711[95%CI: 0.697–0.726]) and was adopted in the feature selection and the establishment of the prediction model. The most important variables in Random Forest algorithm included the systolic blood pressure, Urea nitrogen, neutrophil count, glucose, and D-Dimer. Random Forest algorithm was well calibrated (Brier score = 0.133) in the test group, and obtained the highest net benefit in the decision curve analysis.Conclusion: Based on the general situation of patients and clinical variables, a new machine learning algorithm was developed and verified for the individualized prediction of cardiovascular risk in post-preeclamptic women.

scholarly journals An Improved Random Forest Algorithm for Class-Imbalanced Data Classification and its Application in PAD Risk Factors Analysis

2013 ◽  
Vol 7 (1) ◽  
pp. 62-70 ◽  
Author(s):  
Dengju Yao ◽  
Jing Yang ◽  
Xiaojuan Zhan
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Imbalanced Data ◽  
Machine Learning Algorithms ◽  
Majority Voting ◽  
Training Dataset ◽  
Random Forest Algorithm ◽  
Research Subjects ◽  
Minority Class ◽  
Imbalanced Data Classification

The classification problem is one of the important research subjects in the field of machine learning. However, most machine learning algorithms train a classifier based on the assumption that the number of training examples of classes is almost equal. When a classifier was trained on imbalanced data, the performance of the classifier declined clearly. For resolving the class-imbalanced problem, an improved random forest algorithm was proposed based on sampling with replacement. We extracted multiple example subsets randomly with replacement from majority class, and the example number of extracted example subsets is as the same with minority class example dataset. Then, multiple new training datasets were constructed by combining the each exacted majority example subset and minority class dataset respectively, and multiple random forest classifiers were training on these training dataset. For a prediction example, the class was determined by majority voting of multiple random forest classifiers. The experimental results on five groups UCI datasets and a real clinical dataset show that the proposed method could deal with the class-imbalanced data problem and the improved random forest algorithm outperformed original random forest and other methods in literatures.

scholarly journals Phishing Detection Based on Machine Learning and Feature Selection Methods

2019 ◽  
Vol 13 (12) ◽  
pp. 171 ◽  
Author(s):  
Mohammad Almseidin ◽  
AlMaha Abu Zuraiq ◽  
Mouhammd Al-kasassbeh ◽  
Nidal Alnidami
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Random Forest ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Web Pages ◽  
Selection Methods ◽  
Random Forest Algorithm ◽  
Phishing Detection ◽  
Enormous Number

With increasing technology developments, the Internet has become everywhere and accessible by everyone. There are a considerable number of web-pages with different benefits. Despite this enormous number, not all of these sites are legitimate. There are so-called phishing sites that deceive users into serving their interests. This paper dealt with this problem using machine learning algorithms in addition to employing a novel dataset that related to phishing detection, which contains 5000 legitimate web-pages and 5000 phishing ones. In order to obtain the best results, various machine learning algorithms were tested. Then J48, Random forest, and Multilayer perceptron were chosen. Different feature selection tools were employed to the dataset in order to improve the efficiency of the models. The best result of the experiment achieved by utilizing 20 features out of 48 features and applying it to Random forest algorithm. The accuracy was 98.11%.

Computational Method for Identifying Malonylation Sites by Using Random Forest Algorithm

2020 ◽  
Vol 23 (4) ◽  
pp. 304-312
Author(s):  
ShaoPeng Wang ◽  
JiaRui Li ◽  
Xijun Sun ◽  
Yu-Hang Zhang ◽  
Tao Huang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Random Forest ◽  
Learning Algorithms ◽  
Selection Procedure ◽  
Machine Learning Algorithms ◽  
Computational Method ◽  
Feature Subset ◽  
Random Forest Algorithm ◽  
Post Translational Modification

Background: As a newly uncovered post-translational modification on the ε-amino group of lysine residue, protein malonylation was found to be involved in metabolic pathways and certain diseases. Apart from experimental approaches, several computational methods based on machine learning algorithms were recently proposed to predict malonylation sites. However, previous methods failed to address imbalanced data sizes between positive and negative samples. Objective: In this study, we identified the significant features of malonylation sites in a novel computational method which applied machine learning algorithms and balanced data sizes by applying synthetic minority over-sampling technique. Method: Four types of features, namely, amino acid (AA) composition, position-specific scoring matrix (PSSM), AA factor, and disorder were used to encode residues in protein segments. Then, a two-step feature selection procedure including maximum relevance minimum redundancy and incremental feature selection, together with random forest algorithm, was performed on the constructed hybrid feature vector. Results: An optimal classifier was built from the optimal feature subset, which featured an F1-measure of 0.356. Feature analysis was performed on several selected important features. Conclusion: Results showed that certain types of PSSM and disorder features may be closely associated with malonylation of lysine residues. Our study contributes to the development of computational approaches for predicting malonyllysine and provides insights into molecular mechanism of malonylation.

scholarly journals Predicting Canopy Nitrogen Content in Citrus-Trees Using Random Forest Algorithm Associated to Spectral Vegetation Indices from UAV-Imagery

2019 ◽  
Vol 11 (24) ◽  
pp. 2925 ◽  
Author(s):  
Lucas Prado Osco ◽  
Ana Paula Marques Ramos ◽  
Danilo Roberto Pereira ◽  
Érika Akemi Saito Moriya ◽  
Nilton Nobuhiro Imai ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Nitrogen Content ◽  
Learning Algorithms ◽  
Vegetation Indices ◽  
Machine Learning Algorithms ◽  
Spectral Indices ◽  
Random Forest Algorithm ◽  
Spectral Vegetation Indices ◽  
Canopy Nitrogen

The traditional method of measuring nitrogen content in plants is a time-consuming and labor-intensive task. Spectral vegetation indices extracted from unmanned aerial vehicle (UAV) images and machine learning algorithms have been proved effective in assisting nutritional analysis in plants. Still, this analysis has not considered the combination of spectral indices and machine learning algorithms to predict nitrogen in tree-canopy structures. This paper proposes a new framework to infer the nitrogen content in citrus-tree at a canopy-level using spectral vegetation indices processed with the random forest algorithm. A total of 33 spectral indices were estimated from multispectral images acquired with a UAV-based sensor. Leaf samples were gathered from different planting-fields and the leaf nitrogen content (LNC) was measured in the laboratory, and later converted into the canopy nitrogen content (CNC). To evaluate the robustness of the proposed framework, we compared it with other machine learning algorithms. We used 33,600 citrus trees to evaluate the performance of the machine learning models. The random forest algorithm had higher performance in predicting CNC than all models tested, reaching an R2 of 0.90, MAE of 0.341 g·kg−1 and MSE of 0.307 g·kg−1. We demonstrated that our approach is able to reduce the need for chemical analysis of the leaf tissue and optimizes citrus orchard CNC monitoring.

scholarly journals Improving Heart Disease Prediction Using Random Forest and AdaBoost Algorithms

2021 ◽  
Vol 17 (11) ◽  
pp. 60
Author(s):  
Halima EL Hamdaoui ◽  
Said Boujraf ◽  
Nour El Houda Chaoui ◽  
Badr Alami ◽  
Mustapha Maaroufi
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Decision Support ◽  
Random Forest ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Random Forest Algorithm ◽  
Adaboost Algorithm

heart disease is a major cause of death worldwide. Thus, diagnosis and prediction of heart disease remain mandatory. Clinical decision support systems based on machine learning techniques have become the primary tool to assist clinicians and contribute to automated diagnosis. This paper aims to predict heart disease using Random Forest algorithm enhanced with the boosting algorithm Adaboost. The model is trained and tested on University of California Irvine (UCI) Cleveland and Statlog heart disease datasets using the most relevant features 14 attributes. The result shows that Random Forest algorithm combined with AdaBoost algorithm achieved higher accuracy than applying only Radom Forest algorithm, 96.16%, 95.98%, respectively. We compare our suggested model to report machine learning classifiers. Indeed, the obtained result is supporting the efficiency and validity of our model. Besides, the proposed model achieved high accuracy compared to existing studies in the literature that confirmed that a clinical decision support system could be used to predict heart disease based on machine learning algorithms.

Heart Disease Prediction System Using Ensemble of Machine Learning Algorithms

2019 ◽  
Vol 13 ◽  
Author(s):  
Nandhini Abirami R. ◽  
Durai Raj Vincent
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Random Forest ◽  
Learning Algorithms ◽  
Medical Intervention ◽  
Machine Learning Algorithms ◽  
Disease Prediction ◽  
Prediction System ◽  
Random Forest Algorithm ◽  
Medical Field

Background: Diagnosing diseases is an intricate job in medical field. Machine learning when applied to health care is capable of early detection of disease which would aid to provide early medical intervention. In heart disease prediction, machine learning techniques have played a significant role. Analysis of disease has become vital in health care sectors. The massive data collected by healthcare sectors are preprocessed and analyzed to discover the underlying information in the data for effective decision making and to provide proper medical intervention. The success of machine learning in medical industry is its capability in analyzing the huge amount of data gathered by the health sector and its effectiveness in decision making. Since medical field involves too many manual processes it has become necessary to automate these procedures. Remarkable advancements in electronic medical records have made it possible. Diagnosing diseases is an intricate job in medical field. Objective: The objective of this research is to design a robust machine learning algorithm to predict heart disease. The prediction of heart disease is performed using Ensemble of machine learning algorithms. This is to boost the accuracy achieved by individual machine learning algorithms. Method: Heart Disease Prediction System is developed where the user can input the patient details and the prediction for the particular patient is made using the model developed. The model will predict the output to be either normal or risky. Linear Discriminant Analysis (LDA), Classification and Regression Trees (CART), Support Vector Machines (SVM), K-Nearest Neighbors (KNN) and Naïve Bayes classifier are used as base learners. These algorithms are combined using random forest as the meta classifier. Results: The predictions of classifier are combined using random forest algorithm. The accuracy is lifted from 85.53% to 87.64% which is an impressive improvement on accuracy. Conclusion: Various techniques were adopted to preprocess the data to suite the requirement of analysis. Feature selections were made to optimize the performance of machine learning algorithms. Ensemble prediction gave better accuracy when combined using Random forest algorithm as combiner. Better feature selection techniques can be applied to further improve the accuracy.

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