Recursive Partitioning And Regression Trees Research Articles

Body Weight Prediction using Recursive Partitioning and Regression Trees (RPART) Model in Indian Black Bengal Goat Breed: A Machine Learning Approach

Background: Live body weight (BW) of livestock animals is truly mirror image of all activities of genetics, nutrition, production, reproduction and health status. Thus, the knowledge of calculating BW is of great importance to the producer and critical for goat farming and business. However, there is an unavailability of suitable scales, leading to inaccuracies in decision-making. The present work aimed to predict the live BW of Indian Black Bengal goat using certain morphometric data. Methods: The live BW and eight body measurement data from 1427 disease free, non-pregnant goats aged 25.87±10.47 months with 2.78±1.21 number of parity were collected. The data were first subjected to stepwise regression analysis to achieve the best-fitted model for BW prediction by comparing coefficient of determination (R2) and determining the combination of body dimensions that explained variation in the dependent variable. Further, Recursive Partitioning and Regression Trees (RPART) model, a machine learning tool was deployed to predict BW using certain body measurements. Result: The results of stepwise regression model clearly indicated that heart girth (HG) and punch girth (PG) measurements influenced live BW mostly, but the predictive capabilities (Low R2) of this statistical model were low. The stepwise regression model could not satisfactorily predict BW due to the problem of multicollinearity. Out of eight independent variables, the most important variables emerged from RPART were only HG and PG based on the largest reduction in overall sums of squares error. RPART generated a decision tree with minimal expected error to precisely predict live BW. Hence, RPART model was found to provide better predictive result than stepwise regression model in accurately predicting BW from body measurement variables in Black Bengal goats.

PyPLIF HIPPOS and Receptor Ensemble Docking Increase the Prediction Accuracy of the Structure-Based Virtual Screening Protocol Targeting Acetylcholinesterase.

In this article, the upgrading process of the structure-based virtual screening (SBVS) protocol targeting acetylcholinesterase (AChE) previously published in 2017 is presented. The upgraded version of PyPLIF called PyPLIF HIPPOS and the receptor ensemble docking (RED) method using AutoDock Vina were employed to calculate the ensemble protein–ligand interaction fingerprints (ensPLIF) in a retrospective SBVS campaign targeting AChE. A machine learning technique called recursive partitioning and regression trees (RPART) was then used to optimize the prediction accuracy of the protocol by using the ensPLIF values as the descriptors. The best protocol resulting from this research outperformed the previously published SBVS protocol targeting AChE.

Machine-learning derived algorithms for prediction of radiographic progression in early axial spondyloarthritis.

To compare machine learning (ML) to traditional models to predict radiographic progression in patients with early axial spondyloarthritis (axSpA). We carried out a prospective French multicentric DESIR cohort study with 5 years of follow-up that included patients with chronic back pain for <3 years, suggestive of axSpA. Radiographic progression was defined as progression at the spine (increase of at least 1 point of mSASSS scores/2 years) or at the sacroiliac joint (worsening of at least one grade of the mNY score between 2 visits). Statistical analyses were based on patients without any missing data regarding the outcome and variables of interest (295 patients).Traditional modelling: we performed a multivariate logistic regression model (M1); then variable selection with stepwise selection based on Akaike Information Criterion (stepAIC) method (M2), and Least Absolute Shrinkage and Selection Operator (LASSO) method (M3).ML modelling: using "SuperLearner" package on R, we modelled radiographic progression with stepAIC, LASSO, random forest, Discrete Bayesian Additive Regression Trees Samplers (DBARTS), Generalized Additive Models (GAM), multivariate adaptive polynomial spline regression (polymars), Recursive Partitioning And Regression Trees (RPART) and Super Learner. Accuracy of these models was compared based on their 10-fold cross-validated AUC (cv-AUC). 10-fold cv-AUC for traditional models were 0.79 and 0.78 for M2 and M3, respectively. The three best models in the ML algorithms were the GAM, the DBARTS and the Super Learner models, with 10-fold cv-AUC of: 0.77, 0.76 and 0.74, respectively. Two traditional models predicted radiographic progression as good as the eight ML models tested in this population.

Oil Palm Yield Estimation Based on Vegetation and Humidity Indices Generated from Satellite Images and Machine Learning Techniques

Palm oil has become one of the most consumed vegetable oils in the world, and it is a key element in profitable global value chains. In Costa Rica, oil palm cultivation is one of the three crops with the largest occupied agricultural area. The objective of this study was to explain and predict yield in safe time lags for production management by using free-access satellite images. To this end, machine learning methods were performed to a 20-year data set of an oil palm plantation located in the Central Pacific Region of Costa Rica and the corresponding vegetation indices obtained from LANDSAT satellite images. Since the best correlations corresponded to a one-year time lag, the predictive models Random Forest (RF), Least Absolute Shrinkage and Selection Operator (LASSO), Extreme Gradient Boosting (XGBoost), Recursive Partitioning and Regression Trees (RPART), and Neural Network (NN) were built for a Time-lag 1. These models were applied to all genetic material and to the predominant variety (AVROS) separately. While NN showed the best performance for multispecies information (r2 = 0.8139, NSE = 0.8131, RMSE = 0.3437, MAE = 0.2605), RF showed a better fit for AVROS (r2 = 0.8214, NSE = 0.8020, RMSE = 0.3452, MAE = 0.2669). The most relevant vegetation indices (NDMI, MSI) are related to water in the plant. The study also determined that data distribution must be considered for the prediction and evaluation of the oil palm yield in the area under study. The estimation methods of this study provide information on the identification of important variables (NDMI) to characterize palm oil yield. Additionally, it generates a scenario with acceptable uncertainties on the yield forecast one year in advance. This information is of direct interest to the oil palm industry.

Evolutionary Action Score Identifies a Subset of TP53 Mutated Myelodysplastic Syndrome with Favorable Prognosis

Evolutionary Action Score Identifies a Subset of TP53 Mutated Myelodysplastic Syndrome with Favorable Prognosis

Ensemble Modelling of Skipjack Tuna (Katsuwonus pelamis) Habitats in the Western North Pacific Using Satellite Remotely Sensed Data; a Comparative Analysis Using Machine-Learning Models

To examine skipjack tuna’s habitat utilization in the western North Pacific (WNP) we used an ensemble modelling approach, which applied a fisher- derived presence-only dataset and three satellite remote-sensing predictor variables. The skipjack tuna data were compiled from daily point fishing data into monthly composites and re-gridded into a quarter degree resolution to match the environmental predictor variables, the sea surface temperature (SST), sea surface chlorophyll-a (SSC) and sea surface height anomalies (SSHA), which were also processed at quarter degree spatial resolution. Using the sdm package operated in RStudio software, we constructed habitat models over a 9-month period, from March to November 2004, using 17 algorithms, with a 70:30 split of training and test data, with bootstrapping and 10 runs as parameter settings for our models. Model performance evaluation was conducted using the area under the curve (AUC) of the receiver operating characteristic (ROC), the point biserial correlation coefficient (COR), the true skill statistic (TSS) and Cohen’s kappa (k) metrics. We analyzed the response curves for each predictor variable per algorithm, the variable importance information and the ROC plots. Ensemble predictions of habitats were weighted with the TSS metric. Model performance varied across various algorithms, with the Support Vector Machines (SVM), Boosted Regression Trees (BRT), Random Forests (RF), Multivariate Adaptive Regression Splines (MARS), Generalized Additive Models (GAM), Classification and Regression Trees (CART), Multi-Layer Perceptron (MLP), Recursive Partitioning and Regression Trees (RPART), and Maximum Entropy (MAXENT), showing consistently high performance than other algorithms, while the Flexible Discriminant Analysis (FDA), Mixture Discriminant Analysis (MDA), Bioclim (BIOC), Domain (DOM), Maxlike (MAXL), Mahalanobis Distance (MAHA) and Radial Basis Function (RBF) had lower performance. We found inter-algorithm variations in predictor variable responses. We conclude that the multi-algorithm modelling approach enabled us to assess the variability in algorithm performance, hence a data driven basis for building the ensemble model. Given the inter-algorithm variations observed, the ensemble prediction maps indicated a better habitat utilization map of skipjack tuna than would have been achieved by a single algorithm.

Multi-classifier majority voting analyses in provenance studies on iron artefacts

The main objective of this paper is to propose an approach for identification of provenance of archaeological iron artefacts making use of major oxides and trace elements. For this purpose, seven classifiers were built on the basis of the following techniques: Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Random Forests (RF), Naïve Bayes (NB), K-Nearest Neighbours (KNN), Recursive Partitioning and Regression Trees (RPART) and Kernel Discriminant Analysis (KDA). A final assignment of a given observation to a regional class was carried out on the basis of results provided by all classifiers using a majority voting technique. The proposed approach was first tested on experimental slag and then it was applied to actual archaeological data. It is hoped that this method can become part of a new integrated approach which will consider all available types of data, such as major and trace elements and isotopic ratios.

Evaluation of Machine Learning Algorithms for Surface Water Extraction in a Landsat 8 Scene of Nepal

With over 6000 rivers and 5358 lakes, surface water is one of the most important resources in Nepal. However, the quantity and quality of Nepal’s rivers and lakes are decreasing due to human activities and climate change. Despite the advancement of remote sensing technology and the availability of open access data and tools, the monitoring and surface water extraction works has not been carried out in Nepal. Single or multiple water index methods have been applied in the extraction of surface water with satisfactory results. Extending our previous study, the authors evaluated six different machine learning algorithms: Naive Bayes (NB), recursive partitioning and regression trees (RPART), neural networks (NNET), support vector machines (SVM), random forest (RF), and gradient boosted machines (GBM) to extract surface water in Nepal. With three secondary bands, slope, NDVI and NDWI, the algorithms were evaluated for performance with the addition of extra information. As a result, all the applied machine learning algorithms, except NB and RPART, showed good performance. RF showed overall accuracy (OA) and kappa coefficient (Kappa) of 1 for the all the multiband data with the reference dataset, followed by GBM, NNET, and SVM in metrics. The performances were better in the hilly regions and flat lands, but not well in the Himalayas with ice, snow and shadows, and the addition of slope and NDWI showed improvement in the results. Adding single secondary bands is better than adding multiple in most algorithms except NNET. From current and previous studies, it is recommended to separate any study area with and without snow or low and high elevation, then apply machine learning algorithms in original Landsat data or with the addition of slopes or NDWI for better performance.

An Improved Comparison of Chemometric Analyses for the Identification of Acids and Bases With Colorimetric Sensor Arrays.

Colorimetric sensor arrays incorporating red, green, and blue (RGB) image analysis use value changes from multiple sensors for the identification and quantification of various analytes. RGB data can be easily obtained using image analysis software such as ImageJ. Subsequent chemometric analysis is becoming a key component of colorimetric array RGB data analysis, though literature contains mainly principal component analysis (PCA) and hierarchical cluster analysis (HCA). Seeking to expand the chemometric methods toolkit for array analysis, we explored the performance of nine chemometric methods were compared for the task of classifying 631 solutions (0.1 to 3 M) of acetic acid, malonic acid, lysine, and ammonia using an eight sensor colorimetric array. PCA and LDA (linear discriminant analysis) were effective for visualizing the dataset. For classification, linear discriminant analysis (LDA), (k nearest neighbors) KNN, (soft independent modelling by class analogy) SIMCA, recursive partitioning and regression trees (RPART), and hit quality index (HQI) were very effective with each method classifying compounds with over 90% correct assignments. Support vector machines (SVM) and partial least squares - discriminant analysis (PLS-DA) struggled with ~85 and 39% correct assignments, respectively. Additional mathematical treatments of the data set, such as incrementally increasing the exponents, did not improve the performance of LDA and KNN. The literature precedence indicates that the most common methods for analyzing colorimetric arrays are PCA, LDA, HCA, and KNN. To our knowledge, this is the first report of comparing and contrasting several more diverse chemometric methods to analyze the same colorimetric array data.

Deep Learning Accurately Predicts Estrogen Receptor Status in Breast Cancer Metabolomics Data.

Metabolomics holds the promise as a new technology to diagnose highly heterogeneous diseases. Conventionally, metabolomics data analysis for diagnosis is done using various statistical and machine learning based classification methods. However, it remains unknown if deep neural network, a class of increasingly popular machine learning methods, is suitable to classify metabolomics data. Here we use a cohort of 271 breast cancer tissues, 204 positive estrogen receptor (ER+), and 67 negative estrogen receptor (ER−) to test the accuracies of feed-forward networks, a deep learning (DL) framework, as well as six widely used machine learning models, namely random forest (RF), support vector machines (SVM), recursive partitioning and regression trees (RPART), linear discriminant analysis (LDA), prediction analysis for microarrays (PAM), and generalized boosted models (GBM). DL framework has the highest area under the curve (AUC) of 0.93 in classifying ER+/ER– patients, compared to the other six machine learning algorithms. Furthermore, the biological interpretation of the first hidden layer reveals eight commonly enriched significant metabolomics pathways (adjusted P-value <0.05) that cannot be discovered by other machine learning methods. Among them, protein digestion and absorption and ATP-binding cassette (ABC) transporters pathways are also confirmed in integrated analysis between metabolomics and gene expression data in these samples. In summary, deep learning method shows advantages for metabolomics based breast cancer ER status classification, with both the highest prediction accuracy (AUC = 0.93) and better revelation of disease biology. We encourage the adoption of feed-forward networks based deep learning method in the metabolomics research community for classification.

A New and Validated Clinical Prognostic Model (EPI) for Enteropathy-Associated T-cell Lymphoma.

Enteropathy-associated T-cell lymphoma (EATL) is a rare intestinal non-Hodgkin lymphoma with a poor, though variable prognosis. The International Prognostic Index (IPI) and the prognostic index for peripheral T-cell lymphoma (PIT) have limited predictive value for outcome of EATL. The purpose of this study was to develop and validate a prognostic model for EATL, which can identify high-risk patients who need more aggressive therapy. This retrospective multicenter study was based on 92 patients and included 45 patients diagnosed with EATL between 1999 and 2009 from the Netherlands and 47 patients from England and Scotland, diagnosed with EATL between 1994 and 1998. A new EATL prognostic index (EPI) was constructed using the RPART (recursive partitioning and regression trees) procedure. Validation was performed applying the bootstrap method. Three risk groups were distinguished (P < 0.0001): a high-risk group, characterized by the presence of B-symptoms [median overall survival (OS) of 2 months]; an intermediate-risk group, comprising patients without B-symptoms and an IPI score ≥ 2 (7 months); and a low-risk group, representing patients without B-symptoms and an IPI score of 0 to 1 (34 months). Internal validation showed stability of statistical significance and prognostic discrimination. In contrast with the IPI and PIT, the EPI better classified patients in risk groups according to their clinical outcome. Our new, validated, prognostic model EPI accurately predicts survival outcome in EATL and may be used for patient selection for new therapeutic strategies and evaluation of clinical trials.

Diagnostic criteria for headache attributed to temporomandibular disorders.

We assessed and compared the diagnostic accuracy of two sets of diagnostic criteria for headache secondary to temporomandibular disorders (TMD). In 373 headache subjects with TMD, a TMD headache reference standard was defined as: self-reported temple headache, consensus diagnosis of painful TMD and replication of the temple headache using TMD-based provocation tests. Revised diagnostic criteria for Headache attributed to TMD were selected using the RPART (recursive partitioning and regression trees) procedure, and refined in half of the data set. Using the remaining half of the data, the diagnostic accuracy of the revised criteria was compared to that of the International Headache Society's International Classification of Headache Diseases (ICHD)-II criteria A to C for Headache or facial pain attributed to temporomandibular joint (TMJ) disorder. Relative to the TMD headache reference standard, ICHD-II criteria showed sensitivity of 84% and specificity of 33%. The revised criteria for Headache attributed to TMD had sensitivity of 89% with improved specificity of 87% (p < 0.001). These criteria are (1) temple area headache that is changed with jaw movement, function or parafunction and (2) provocation of that headache by temporalis muscle palpation or jaw movement. Having significantly better specificity than the ICHD-II criteria A to C, the revised criteria are recommended to diagnose headache secondary to TMD.

Sensitivity analysis for complex ecological models – A new approach

A strategy for global sensitivity analysis of a multi-parameter ecological model was developed and used for the hydrodynamic-ecological model (DYRESM–CAEDYM, DYnamic REservoir Simulation Model- Computational Aquatic Ecosystem Dynamics Model) applied to Lake Kinneret (Israel). Two different methods of sensitivity analysis, RPART (Recursive Partitioning And Regression Trees) and GLM (General Linear Model) were applied in order to screen a subset of significant parameters. All the parameters which were found significant by at least one of these methods were entered as input to a GBM (Generalized Boosted Modeling) analysis in order to provide a quantitative measure of the sensitivity of the model variables to these parameters. Although the GBM is a general and powerful machine learning algorithm, it has substantial computational costs in both storage requirements and CPU time. Employing the screening stage reduces this cost. The results of the analysis highlighted the role of particulate organic material in the lake ecosystem and its impact on the over all lake nutrient budget. The GBM analysis established, for example, that parameters such as particulate organic material diameter and density were particularly important to the model outcomes. The results were further explored by lumping together output variables that are associated with sub-components of the ecosystem. The variable lumping approach suggested that the phytoplankton group is most sensitive to parameters associated with the dominant phytoplankton group, dinoflagellates, and with nanoplankton (Chlorophyta), supporting the view of Lake Kinneret as a bottom–up system. The study demonstrates the effectiveness of such procedures for extracting useful information for model calibration and guiding further data collection.

Bone fracture risk estimation based on image similarity

We propose a fracture risk estimation technique based on image similarity. We employ image similarity indices to determine how images are similar to each other in their 3D bone mineral density distributions. Our premise for fracture risk estimation is that if a given scan is more similar to scans of subjects known to have fractures than to scans of control subjects, this subject is likely to have a higher degree of fracture risk. To test this hypothesis, we analyzed hip QCT scans of 37 patients with hip fractures and 38 age-matched controls. We divided the scans randomly into two groups: the Model Group and the Test Group. For each scan in the Test Group, the difference between the mean value of its image similarities to the Model fracture group and the mean value of its image similarities to the Model control group was used as index of fracture risk. We then used the estimated fracture risk indices to discriminate the fractured patients and controls in the Test Group. A test scan with a larger mean value of image similarities with respect to the Model fracture group was classified as a scan from a fractured patient, otherwise it was classified as a scan from a control subject. Based on ROC analysis, we compared the discrimination performances using image similarity measures with that obtained by using bone mineral density (BMD). When using BMD measured in the femoral neck, with the optimal BMD cutoff, the sensitivity and specificity were 86.5% and 73.7%. For the image similarity measures, the sensitivity ranged between 86.5% and 100%, and specificity ranged between 63.2% and 76.3%. By combining BMD with image similarity measures, the sensitivity and specificity reached 94.6% and 76.3% using linear discriminant analysis (LDA) algorithm, or 91.9% and 81.6% using recursive partitioning and regression trees (RPART) algorithm. In the RPART approach, the AUC value of the ROC curve was 0.923, higher than the AUC value of 0.835 when using BMD alone ( p-value: 0.0046). Our results showed that combining BMD with image similarity measures resulted in improved hip fracture risk estimation.

Heidelberg Retina Tomograph 3 machine learning classifiers for glaucoma detection

Aims:To assess performance of classifiers trained on Heidelberg Retina Tomograph 3 (HRT3) parameters for discriminating between healthy and glaucomatous eyes.Methods:Classifiers were trained using HRT3 parameters from 60 healthy subjects and...