Supervised machine learning models based on support vector regression

2020 ◽  
pp. 41-60
Author(s):  
Harsh S. Dhiman ◽  
Dipankar Deb ◽  
Valentina Emilia Balas
Keyword(s):  
Machine Learning ◽  
Support Vector Regression ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Learning Models ◽  
Machine Learning Models

scholarly journals Application of Natural Language Processing with Supervised Machine Learning Techniques to Predict the Overall Drugs Performance

2020 ◽  
Vol 11 (40) ◽  
pp. 8-23
Author(s):  
Pius MARTHIN ◽  
Duygu İÇEN
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Random Forest ◽  
Semantic Analysis ◽  
Classification Tree ◽  
Supervised Machine Learning ◽  
Training Dataset ◽  
Support Vector ◽  
Learning Models ◽  
Machine Learning Models

Online product reviews have become a valuable source of information which facilitate customer decision with respect to a particular product. With the wealthy information regarding user's satisfaction and experiences about a particular drug, pharmaceutical companies make the use of online drug reviews to improve the quality of their products. Machine learning has enabled scientists to train more efficient models which facilitate decision making in various fields. In this manuscript we applied a drug review dataset used by (Gräβer, Kallumadi, Malberg,& Zaunseder, 2018), available freely from machine learning repository website of the University of California Irvine (UCI) to identify best machine learning model which provide a better prediction of the overall drug performance with respect to users' reviews. Apart from several manipulations done to improve model accuracy, all necessary procedures required for text analysis were followed including text cleaning and transformation of texts to numeric format for easy training machine learning models. Prior to modeling, we obtained overall sentiment scores for the reviews. Customer's reviews were summarized and visualized using a bar plot and word cloud to explore the most frequent terms. Due to scalability issues, we were able to use only the sample of the dataset. We randomly sampled 15000 observations from the 161297 training dataset and 10000 observations were randomly sampled from the 53766 testing dataset. Several machine learning models were trained using 10 folds cross-validation performed under stratified random sampling. The trained models include Classification and Regression Trees (CART), classification tree by C5.0, logistic regression (GLM), Multivariate Adaptive Regression Spline (MARS), Support vector machine (SVM) with both radial and linear kernels and a classification tree using random forest (Random Forest). Model selection was done through a comparison of accuracies and computational efficiency. Support vector machine (SVM) with linear kernel was significantly best with an accuracy of 83% compared to the rest. Using only a small portion of the dataset, we managed to attain reasonable accuracy in our models by applying the TF-IDF transformation and Latent Semantic Analysis (LSA) technique to our TDM.

scholarly journals Artificial Intelligence Learning Semantics via External Resources for Classifying Diagnosis Codes in Discharge Notes (Preprint)

2017 ◽  
Author(s):  
Chin Lin ◽  
Chia-Jung Hsu ◽  
Yu-Sheng Lou ◽  
Shih-Jen Yeh ◽  
Chia-Cheng Lee ◽  
...  
Keyword(s):  
Machine Learning ◽  
Word Embedding ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Free Text ◽  
Learning Models ◽  
Diagnosis Codes ◽  
Icd 10 ◽  
F Measure ◽  
Machine Learning Models

BACKGROUND Automated disease code classification using free-text medical information is important for public health surveillance. However, traditional natural language processing (NLP) pipelines are limited, so we propose a method combining word embedding with a convolutional neural network (CNN). OBJECTIVE Our objective was to compare the performance of traditional pipelines (NLP plus supervised machine learning models) with that of word embedding combined with a CNN in conducting a classification task identifying International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) diagnosis codes in discharge notes. METHODS We used 2 classification methods: (1) extracting from discharge notes some features (terms, n-gram phrases, and SNOMED CT categories) that we used to train a set of supervised machine learning models (support vector machine, random forests, and gradient boosting machine), and (2) building a feature matrix, by a pretrained word embedding model, that we used to train a CNN. We used these methods to identify the chapter-level ICD-10-CM diagnosis codes in a set of discharge notes. We conducted the evaluation using 103,390 discharge notes covering patients hospitalized from June 1, 2015 to January 31, 2017 in the Tri-Service General Hospital in Taipei, Taiwan. We used the receiver operating characteristic curve as an evaluation measure, and calculated the area under the curve (AUC) and F-measure as the global measure of effectiveness. RESULTS In 5-fold cross-validation tests, our method had a higher testing accuracy (mean AUC 0.9696; mean F-measure 0.9086) than traditional NLP-based approaches (mean AUC range 0.8183-0.9571; mean F-measure range 0.5050-0.8739). A real-world simulation that split the training sample and the testing sample by date verified this result (mean AUC 0.9645; mean F-measure 0.9003 using the proposed method). Further analysis showed that the convolutional layers of the CNN effectively identified a large number of keywords and automatically extracted enough concepts to predict the diagnosis codes. CONCLUSIONS Word embedding combined with a CNN showed outstanding performance compared with traditional methods, needing very little data preprocessing. This shows that future studies will not be limited by incomplete dictionaries. A large amount of unstructured information from free-text medical writing will be extracted by automated approaches in the future, and we believe that the health care field is about to enter the age of big data.

scholarly journals COVID-19 Future Predictions Using 4 Supervised Machine Learning Models

2022 ◽  
pp. 61-66
Author(s):  
Aditi Vadhavkar ◽  
Pratiksha Thombare ◽  
Priyanka Bhalerao ◽  
Utkarsha Auti
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Support Vector Machine ◽  
Supervised Machine Learning ◽  
Perioperative Outcomes ◽  
Support Vector ◽  
Learning Models ◽  
Death Rates ◽  
The World ◽  
Machine Learning Models

Forecasting Mechanisms like Machine Learning (ML) models having been proving their significance to anticipate perioperative outcomes in the domain of decision making on the future course of actions. Many application domains have witnessed the use of ML models for identification and prioritization of adverse factors for a threat. The spread of COVID-19 has proven to be a great threat to a mankind announcing it a worldwide pandemic throughout. Many assets throughout the world has faced enormous infectivity and contagiousness of this illness. To look at the figure of undermining components of COVID-19 we’ve specifically used four Machine Learning Models Linear Regression (LR), Least shrinkage and determination administrator (LASSO), Support vector machine (SVM) and Exponential smoothing (ES). The results depict that the ES performs best among the four models employed in this study, followed by LR and LASSO which performs well in forecasting the newly confirmed cases, death rates yet recovery rates, but SVM performs poorly all told the prediction scenarios given the available dataset.

A Novel Approach of Using Feature-Based Machine Learning Models to Expand Coverage of Oil Saturation from Dielectric Logs

2021 ◽  
Author(s):  
Mohammed Alghazal ◽  
Dimitrios Krinis
Keyword(s):  
Machine Learning ◽  
Supervised Machine Learning ◽  
Water Salinity ◽  
Support Vector ◽  
Petrophysical Properties ◽  
Learning Models ◽  
Heat Map ◽  
Oil Saturation ◽  
Log Data ◽  
Machine Learning Models

Abstract Dielectric log is a specialized tool with proprietary procedures to predict oil saturation independent of water salinity. Conventional resistivity logging is more routinely used but dependent on water salinity and Archie's parameters, leading to high measurement uncertainty in mixed salinity environments. This paper presents a novel machine learning approach of propagating the coverage of dielectric-based oil saturation driven by features extracted from commonly available reservoir information, petrophysical properties and conventional log data. More than 20 features were extracted from several sources. Based on sampling frequency, extracted features are divided into well-based discrete features and petrophysical-based continuous features. Examples of well-based features include well location with respect to flank (east or west), fluid viscosities and densities, total dissolved solids from surface water, distance to nearest water injector and injection volume. Petrophysical-based features include height above free water level (HAFWL), porosity, modelled permeability, initial water saturation, resistivity-based saturation, rock-type and caliper. In addition, we engineered two new depth-related and continuous features, we call them Height-Below-Crest (HBC) and Height-Above-Top-Injector-Zone (HATIZ). Initial data exploration was performed using Pearson's correlation heat map. Fluid densities and viscosities show strong correlation (60-80%) to the engineered features (HBC and HATIZ), which helped to capture the viscous and gravity forces effect across the well's vertical depth. The heat map also shows weak correlation between the features and the target variable, the oil saturation from dielectric log. The dataset, with 5000 samples, was randomly split into 80% training and 20% testing. A robust scaling technique to outliers is used to scale the features prior to modeling. The preliminary performance of various supervised machine learning models, including decision trees, ensemble methods, neural network and support vector machines, were benchmarked using K-Fold cross-validation on the training data prior to testing. Ensemble-based methods, random forest and gradient boosting, produced the least mean absolute error compared to other methods and thus were selected for further hyper-parameter tuning. Exhaustive grid search was performed on both models to find the best-fit parameters, achieving a correlation coefficient of 70% on the testing dataset. Features analysis indicate that the engineered features, HBC and HATIZ, along with the porosity, HAFWL and resistivity-based saturation are the most importance features for predicting the oil saturation from dielectric log. Dielectric log provides an edge over resistivity-based logging technique in mixed salinity formations, but with more elaborate interpretation procedures. In this paper, we present a soft-computing and economical alternative of using ensemble machine learning models to predict oil saturation from dielectric log given some extracted features from common reservoir information, petrophysical properties and conventional log data.

scholarly journals Assessment of the Different Machine Learning Models for Prediction of Cluster Bean (Cyamopsis tetragonoloba L. Taub.) Yield

2020 ◽  
pp. 98-105
Author(s):  
Darshan Jagannath Pangarkar ◽  
Rajesh Sharma ◽  
Amita Sharma ◽  
Madhu Sharma
Keyword(s):  
Machine Learning ◽  
Support Vector Regression ◽  
Cross Validation ◽  
Support Vector ◽  
Linear Kernel ◽  
Learning Models ◽  
Degree Polynomial ◽  
Random Forest Regression ◽  
Machine Learning Models ◽  
Second Degree

Prediction of crop yield can help traders, agri-business and government agencies to plan their activities accordingly. It can help government agencies to manage situations like over or under production. Traditionally statistical and crop simulation methods are used for this purpose. Machine learning models can be great deal of help. Aim of present study is to assess the predictive ability of various machine learning models for Cluster bean (Cyamopsis tetragonoloba L. Taub.) yield prediction. Various machine learning models were applied and tested on panel data of 19 years i.e. from 1999-2000 to 2017-18 for the Bikaner district of Rajasthan. Various data mining steps were performed before building a model. K- Nearest Nighbors (K-NN), Support Vector Regression (SVR) with various kernels, and Random forest regression were applied. Cross validation was also performed to know extra sampler validity. The best fitted model was chosen based cross validation scores and R2 values. Besides the coefficient of determination (R2), root mean squared error (RMSE), mean absolute error (MAE), and root relative squared error (RRSE) were calculated for the testing set. Support vector regression with linear kernel has the lowest RMSE (23.19), RRSE (0.14), MAE (19.27) values followed by random forest regression and second-degree polynomial support vector regression with the value of gamma = auto. Instead there was a little difference with R2, placing support vector regression first (98.31%), followed by second-degree polynomial support vector regression with value of gamma = auto (89.83%) and second-degree polynomial support vector regression with value of gamma = scale (88.83%). On two-fold cross validation, support vector regression with a linear kernel had the highest cross validation score explaining 71% (+/-0.03) followed by second-degree polynomial support vector regression with a value of gamma = auto and random forest regression. KNN and support vector regression with radial basis function as a kernel function had negative cross validation scores. Support vector regression with linear kernel was found to be the best-fitted model for predicting the yield as it had higher sample validity (98.31%) and global validity (71%).

scholarly journals An Automated System for ECG Arrhythmia Detection Using Machine Learning Techniques

2021 ◽  
Vol 10 (22) ◽  
pp. 5450
Author(s):  
Mohamed Sraitih ◽  
Younes Jabrane ◽  
Amir Hajjam El Hassani
Keyword(s):  
Machine Learning ◽  
Supervised Machine Learning ◽  
Automated System ◽  
Support Vector ◽  
Classification Methods ◽  
Clinical Environment ◽  
Learning Models ◽  
Bundle Branch Block ◽  
Ecg Arrhythmia ◽  
Machine Learning Models

The new advances in multiple types of devices and machine learning models provide opportunities for practical automatic computer-aided diagnosis (CAD) systems for ECG classification methods to be practicable in an actual clinical environment. This imposes the requirements for the ECG arrhythmia classification methods that are inter-patient. We aim in this paper to design and investigate an automatic classification system using a new comprehensive ECG database inter-patient paradigm separation to improve the minority arrhythmical classes detection without performing any features extraction. We investigated four supervised machine learning models: support vector machine (SVM), k-nearest neighbors (KNN), Random Forest (RF), and the ensemble of these three methods. We test the performance of these techniques in classifying: Normal beat (NOR), Left Bundle Branch Block Beat (LBBB), Right Bundle Branch Block Beat (RBBB), Premature Atrial Contraction (PAC), and Premature Ventricular Contraction (PVC), using inter-patient real ECG records from MIT-DB after segmentation and normalization of the data, and measuring four metrics: accuracy, precision, recall, and f1-score. The experimental results emphasized that with applying no complicated data pre-processing or feature engineering methods, the SVM classifier outperforms the other methods using our proposed inter-patient paradigm, in terms of all metrics used in experiments, achieving an accuracy of 0.83 and in terms of computational cost, which remains a very important factor in implementing classification models for ECG arrhythmia. This method is more realistic in a clinical environment, where varieties of ECG signals are collected from different patients.

scholarly journals Prediction of cancer incidence rates for the European continent using machine learning models

2021 ◽  
Vol 27 (1) ◽  
pp. 146045822098387
Author(s):  
Boran Sekeroglu ◽  
Kubra Tuncal
Keyword(s):  
Neural Network ◽  
Colorectal Cancer ◽  
Machine Learning ◽  
Linear Regression ◽  
Support Vector Regression ◽  
Incidence Rates ◽  
Support Vector ◽  
Learning Models ◽  
European Continent ◽  
Machine Learning Models

Cancer is one of the most important and common public health problems on Earth that can occur in many different types. Treatments and precautions are aimed at minimizing the deaths caused by cancer; however, incidence rates continue to rise. Thus, it is important to analyze and estimate incidence rates to support the determination of more effective precautions. In this research, 2018 Cancer Datasheet of World Health Organization (WHO), is used and all countries on the European Continent are considered to analyze and predict the incidence rates until 2020, for Lung cancer, Breast cancer, Colorectal cancer, Prostate cancer and All types of cancer, which have highest incidence and mortality rates. Each cancer type is trained by six machine learning models namely, Linear Regression, Support Vector Regression, Decision Tree, Long-Short Term Memory neural network, Backpropagation neural network, and Radial Basis Function neural network according to gender types separately. Linear regression and support vector regression outperformed the other models with the [Formula: see text] scores 0.99 and 0.98, respectively, in initial experiments, and then used for prediction of incidence rates of the considered cancer types. The ML models estimated that the maximum rise of incidence rates would be in colorectal cancer for females by 6%.

scholarly journals Using Machine Learning Models and Actual Transaction Data for Predicting Real Estate Prices

2020 ◽  
Vol 10 (17) ◽  
pp. 5832 ◽  
Author(s):  
Ping-Feng Pai ◽  
Wen-Chang Wang
Keyword(s):  
Machine Learning ◽  
Real Estate ◽  
Least Squares ◽  
Support Vector Regression ◽  
Support Vector ◽  
Learning Models ◽  
Transaction Data ◽  
Price Prediction ◽  
Real Estate Prices ◽  
Machine Learning Models

Real estate price prediction is crucial for the establishment of real estate policies and can help real estate owners and agents make informative decisions. The aim of this study is to employ actual transaction data and machine learning models to predict prices of real estate. The actual transaction data contain attributes and transaction prices of real estate that respectively serve as independent variables and dependent variables for machine learning models. The study employed four machine learning models-namely, least squares support vector regression (LSSVR), classification and regression tree (CART), general regression neural networks (GRNN), and backpropagation neural networks (BPNN), to forecast real estate prices. In addition, genetic algorithms were used to select parameters of machine learning models. Numerical results indicated that the least squares support vector regression outperforms the other three machine learning models in terms of forecasting accuracy. Furthermore, forecasting results generated by the least squares support vector regression are superior to previous related studies of real estate price prediction in terms of the average absolute percentage error. Thus, the machine learning-based model is a substantial and feasible way to forecast real estate prices, and the least squares support vector regression can provide relatively competitive and satisfactory results.

Application of Machine Learning Techniques to Predict Binding Affinity for Drug Targets: A Study of Cyclin-Dependent Kinase 2

2020 ◽  
Vol 28 (2) ◽  
pp. 253-265 ◽  
Author(s):  
Gabriela Bitencourt-Ferreira ◽  
Amauri Duarte da Silva ◽  
Walter Filgueira de Azevedo
Keyword(s):  
Machine Learning ◽  
Binding Affinity ◽  
Predictive Performance ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Scoring Functions ◽  
Cyclin Dependent Kinase ◽  
Learning Models ◽  
Learning Techniques ◽  
Machine Learning Models

Background: The elucidation of the structure of cyclin-dependent kinase 2 (CDK2) made it possible to develop targeted scoring functions for virtual screening aimed to identify new inhibitors for this enzyme. CDK2 is a protein target for the development of drugs intended to modulate cellcycle progression and control. Such drugs have potential anticancer activities. Objective: Our goal here is to review recent applications of machine learning methods to predict ligand- binding affinity for protein targets. To assess the predictive performance of classical scoring functions and targeted scoring functions, we focused our analysis on CDK2 structures. Methods: We have experimental structural data for hundreds of binary complexes of CDK2 with different ligands, many of them with inhibition constant information. We investigate here computational methods to calculate the binding affinity of CDK2 through classical scoring functions and machine- learning models. Results: Analysis of the predictive performance of classical scoring functions available in docking programs such as Molegro Virtual Docker, AutoDock4, and Autodock Vina indicated that these methods failed to predict binding affinity with significant correlation with experimental data. Targeted scoring functions developed through supervised machine learning techniques showed a significant correlation with experimental data. Conclusion: Here, we described the application of supervised machine learning techniques to generate a scoring function to predict binding affinity. Machine learning models showed superior predictive performance when compared with classical scoring functions. Analysis of the computational models obtained through machine learning could capture essential structural features responsible for binding affinity against CDK2.

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