The Classification of Profiles of Financial Catastrophe Caused by Out-of-Pocket Payments: A Methodological Approach

The financial catastrophe resulting from the out-of-pocket payments necessary to access and use healthcare systems has been widely studied in the literature. The aim of this work is to predict the impact of the financial catastrophe a household will face as a result of out-of-pocket payments in long-term care in Spain. These predictions were made using machine learning techniques such as LASSO (Least Absolute Shrinkage and Selection Operator) penalized regression and elastic-net, as well as algorithms like k-nearest neighbors (KNN), MARS (Multivariate Adaptive Regression Splines), random forest, boosted trees and SVM (Support Vector Machine). The results reveal that all the classification methods performed well, with the complex models performing better than the simpler ones and showing no evidence of overfitting. Detecting and defining the profiles of individuals and families most likely to suffer from financial catastrophe is crucial in enabling the design of financial policies aimed at protecting vulnerable groups.

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Development of Machine Learning Models to Evaluate the Toughness of OPH Alloys

Materials ◽

10.3390/ma14216713 ◽

2021 ◽

Vol 14 (21) ◽

pp. 6713

Author(s):

Omid Khalaj ◽

Moslem Ghobadi ◽

Ehsan Saebnoori ◽

Alireza Zarezadeh ◽

Mohammadreza Shishesaz ◽

...

Keyword(s):

Machine Learning ◽

Mechanical Properties ◽

Mechanical Alloying ◽

Fuzzy Inference ◽

Oxide Dispersion Strengthened ◽

Machine Learning Techniques ◽

Support Vector ◽

Anfis Model ◽

Inference Systems ◽

The Impact

Oxide Precipitation-Hardened (OPH) alloys are a new generation of Oxide Dispersion-Strengthened (ODS) alloys recently developed by the authors. The mechanical properties of this group of alloys are significantly influenced by the chemical composition and appropriate heat treatment (HT). The main steps in producing OPH alloys consist of mechanical alloying (MA) and consolidation, followed by hot rolling. Toughness was obtained from standard tensile test results for different variants of OPH alloy to understand their mechanical properties. Three machine learning techniques were developed using experimental data to simulate different outcomes. The effectivity of the impact of each parameter on the toughness of OPH alloys is discussed. By using the experimental results performed by the authors, the composition of OPH alloys (Al, Mo, Fe, Cr, Ta, Y, and O), HT conditions, and mechanical alloying (MA) were used to train the models as inputs and toughness was set as the output. The results demonstrated that all three models are suitable for predicting the toughness of OPH alloys, and the models fulfilled all the desired requirements. However, several criteria validated the fact that the adaptive neuro-fuzzy inference systems (ANFIS) model results in better conditions and has a better ability to simulate. The mean square error (MSE) for artificial neural networks (ANN), ANFIS, and support vector regression (SVR) models was 459.22, 0.0418, and 651.68 respectively. After performing the sensitivity analysis (SA) an optimized ANFIS model was achieved with a MSE value of 0.003 and demonstrated that HT temperature is the most significant of these parameters, and this acts as a critical rule in training the data sets.

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Predictive models for stage and risk classification in head and neck squamous cell carcinoma (HNSCC)

PeerJ ◽

10.7717/peerj.9656 ◽

2020 ◽

Vol 8 ◽

pp. e9656

Author(s):

Sugandh Kumar ◽

Srinivas Patnaik ◽

Anshuman Dixit

Keyword(s):

Machine Learning ◽

Expression Profiles ◽

Disease Process ◽

Penalized Regression ◽

Functional Enrichment ◽

Machine Learning Techniques ◽

Gradient Boosting ◽

Support Vector ◽

Sequencing Data ◽

Therapeutic Modalities

Machine learning techniques are increasingly used in the analysis of high throughput genome sequencing data to better understand the disease process and design of therapeutic modalities. In the current study, we have applied state of the art machine learning (ML) algorithms (Random Forest (RF), Support Vector Machine Radial Kernel (svmR), Adaptive Boost (AdaBoost), averaged Neural Network (avNNet), and Gradient Boosting Machine (GBM)) to stratify the HNSCC patients in early and late clinical stages (TNM) and to predict the risk using miRNAs expression profiles. A six miRNA signature was identified that can stratify patients in the early and late stages. The mean accuracy, sensitivity, specificity, and area under the curve (AUC) was found to be 0.84, 0.87, 0.78, and 0.82, respectively indicating the robust performance of the generated model. The prognostic signature of eight miRNAs was identified using LASSO (least absolute shrinkage and selection operator) penalized regression. These miRNAs were found to be significantly associated with overall survival of the patients. The pathway and functional enrichment analysis of the identified biomarkers revealed their involvement in important cancer pathways such as GP6 signalling, Wnt signalling, p53 signalling, granulocyte adhesion, and dipedesis. To the best of our knowledge, this is the first such study and we hope that these signature miRNAs will be useful for the risk stratification of patients and the design of therapeutic modalities.

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Recognition of Emotion According to the Physical Elements of the Video

Sensors ◽

10.3390/s20030649 ◽

2020 ◽

Vol 20 (3) ◽

pp. 649 ◽

Cited By ~ 2

Author(s):

Jing Zhang ◽

Xingyu Wen ◽

Mincheol Whang

Keyword(s):

Video Production ◽

Support Vector ◽

Two Dimensional ◽

K Nearest Neighbors ◽

Four Dimensions ◽

Emotion Elicitation ◽

Neural Processes ◽

Physical Elements ◽

The Impact ◽

The Relationship

The increasing interest in the effects of emotion on cognitive, social, and neural processes creates a constant need for efficient and reliable techniques for emotion elicitation. Emotions are important in many areas, especially in advertising design and video production. The impact of emotions on the audience plays an important role. This paper analyzes the physical elements in a two-dimensional emotion map by extracting the physical elements of a video (color, light intensity, sound, etc.). We used k-nearest neighbors (K-NN), support vector machine (SVM), and multilayer perceptron (MLP) classifiers in the machine learning method to accurately predict the four dimensions that express emotions, as well as summarize the relationship between the two-dimensional emotion space and physical elements when designing and producing video.

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Prediction of Liver Diseases by Using Few Machine Learning Based Approaches

Australian Journal of Engineering and Innovative Technology ◽

10.34104/ajeit.020.085090 ◽

2020 ◽

pp. 85-90

Keyword(s):

Machine Learning ◽

Comparative Analysis ◽

Liver Diseases ◽

Model Building ◽

Medical Science ◽

Machine Learning Techniques ◽

Support Vector ◽

Classification Algorithms ◽

K Nearest Neighbors ◽

Learning Techniques

Advancement in medical science has always been one of the most vital aspects of the human race. With the progress in technology, the use of modern techniques and equipment is always imposed on treatment purposes. Nowadays, machine learning techniques have widely been used in medical science for assuring accuracy. In this work, we have constructed computational model building techniques for liver disease prediction accurately. We used some efficient classification algorithms: Random Forest, Perceptron, Decision Tree, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM) for predicting liver diseases. Our works provide the implementation of hybrid model construction and comparative analysis for improving prediction performance. At first, classification algorithms are applied to the original liver patient datasets collected from the UCI repository. Then we analyzed features and tweaked to improve the performance of our predictor and made a comparative analysis among the classifiers. We examined that, KNN algorithm outperformed all other techniques with feature selection.

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The Effect of Weather in Soccer Results: An Approach Using Machine Learning Techniques

Applied Sciences ◽

10.3390/app10196750 ◽

2020 ◽

Vol 10 (19) ◽

pp. 6750

Author(s):

Ditsuhi Iskandaryan ◽

Francisco Ramos ◽

Denny Asarias Palinggi ◽

Sergio Trilles

Keyword(s):

Support Vector Machine ◽

Nearest Neighbors ◽

Research Community ◽

Machine Learning Techniques ◽

Weather Data ◽

Support Vector ◽

K Nearest Neighbors ◽

Task Support ◽

Extremely Randomized Trees ◽

Learning Techniques

The growing popularity of soccer has led to the prediction of match results becoming of interest to the research community. The aim of this research is to detect the effects of weather on the result of matches by implementing Random Forest, Support Vector Machine, K-Nearest Neighbors Algorithm, and Extremely Randomized Trees Classifier. The analysis was executed using the Spanish La Liga and Segunda division from the seasons 2013–2014 to 2017–2018 in combination with weather data. Two tasks were proposed as part of this study: the first was to find out whether the game will end in a draw, a win by the hosts or a victory by the guests, and the second was to determine whether the match will end in a draw or if one of the teams will win. The results show that, for the first task, Extremely Randomized Trees Classifier is a better method, with an accuracy of 65.9%, and, for the second task, Support Vector Machine yielded better results with an accuracy of 79.3%. Moreover, it is possible to predict whether the game will end in a draw or not with 0.85 AUC-ROC. Additionally, for comparative purposes, the analysis was also performed without weather data.

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Development of Equipment and Application of Machine Learning Techniques Using Frequency Response Data for Cap Damage Detection of Porcelain Insulators

Applied Sciences ◽

10.3390/app10082820 ◽

2020 ◽

Vol 10 (8) ◽

pp. 2820 ◽

Cited By ~ 1

Author(s):

In Hyuk Choi ◽

Ja Bin Koo ◽

Ju Am Son ◽

Jun Sin Yi ◽

Young Geun Yoon ◽

...

Keyword(s):

Machine Learning ◽

Damage Detection ◽

Frequency Response ◽

Dynamic Characteristics ◽

Machine Learning Techniques ◽

Support Vector ◽

Physical Damage ◽

Long Distance ◽

Porcelain Insulators ◽

The Impact

The most common method for inspection of insulators is to measure the change of electrical characteristics such as electric resistance and partial discharge. However, even if there is no physical damage, these values vary depending on the temperature, humidity, and chloride content of the atmosphere. In this respect, an alternative to such methods can be the impact response test, and a frequency response function (FRF) obtained from the test has been widely used as a tool for damage detection. In this study the FRF was applied to identify the cap damage of porcelain insulators. In addition, to solve the danger of high voltage and poor field accessibility near the insulator, a device with high field applicability was developed to measure FRF from a long distance using an auto impact hammer and Micro Electro Mechanical Systems (MEMS) technology. Even though the FRF is most suitable for inspection of porcelain insulators, dynamic characteristics such as natural frequencies may vary depending on manufacturing errors, installation conditions, etc., which may cause difficulties in damage identification. To overcome this limitation, the machine learning (ML) method was applied in this study to provide a diagnostic method that ensured consistent and accurate judgment. As a result of predicting the normal and the cap damage data using the support vector machine (SVM), bagging, k-nearest neighbor (kNN), and discriminant analysis (DA) methods, the overall F1 score was over 87% and the bagging method achieved the highest accuracy. In this study, the frequency range and dynamic characteristics that are sensitive to the physical damage of the insulator were derived and, based on this, the optimum ML methods with improved equipment could provide analysis with higher accuracy and consistency than general analysis using the FRF.

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Computational Identification of Chemical Compounds with Potential Activity against Leishmania amazonensis using Nonlinear Machine Learning Techniques

Current Topics in Medicinal Chemistry ◽

10.2174/1568026619666181130121558 ◽

2019 ◽

Vol 18 (27) ◽

pp. 2347-2354 ◽

Cited By ~ 3

Author(s):

Juan Alberto Castillo-Garit ◽

Naivi Flores-Balmaseda ◽

Orlando Álvarez ◽

Hai Pham-The ◽

Virginia Pérez-Doñate ◽

...

Keyword(s):

Machine Learning ◽

Computational Models ◽

Neglected Tropical Diseases ◽

Chemical Compounds ◽

Theoretical Models ◽

Leishmania Amazonensis ◽

Machine Learning Techniques ◽

Support Vector ◽

K Nearest Neighbors ◽

Data Set

Leishmaniasis is a poverty-related disease endemic in 98 countries worldwide, with morbidity and mortality increasing daily. All currently used first-line and second-line drugs for the treatment of leishmaniasis exhibit several drawbacks including toxicity, high costs and route of administration. Consequently, the development of new treatments for leishmaniasis is a priority in the field of neglected tropical diseases. The aim of this work is to develop computational models those allow the identification of new chemical compounds with potential anti-leishmanial activity. A data set of 116 organic chemicals, assayed against promastigotes of Leishmania amazonensis, is used to develop the theoretical models. The cutoff value to consider a compound as active one was IC50≤1.5μM. For this study, we employed Dragon software to calculate the molecular descriptors and WEKA to obtain machine learning (ML) models. All ML models showed accuracy values between 82% and 91%, for the training set. The models developed with k-nearest neighbors and classification trees showed sensitivity values of 97% and 100%, respectively; while the models developed with artificial neural networks and support vector machine showed specificity values of 94% and 92%, respectively. In order to validate our models, an external test-set was evaluated with good behavior for all models. A virtual screening was performed and 156 compounds were identified as potential anti-leishmanial by all the ML models. This investigation highlights the merits of ML-based techniques as an alternative to other more traditional methods to find new chemical compounds with anti-leishmanial activity.

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Detection of Loss Zones while Drilling Using Different Machine Learning Techniques

Journal of Energy Resources Technology ◽

10.1115/1.4051553 ◽

2021 ◽

pp. 1-29

Author(s):

Ahmed Alsaihati ◽

Mahmoud Abughaban ◽

Salaheldin Elkatatny ◽

Abdulazeez Abdulraheem

Keyword(s):

Machine Learning ◽

Support Vector Machines ◽

Random Forests ◽

Nearest Neighbors ◽

Machine Learning Techniques ◽

Support Vector ◽

K Nearest Neighbors ◽

Learning Techniques ◽

Vector Machines ◽

Testing Set

Abstract Fluid loss into formations is a common operational issue that is frequently encountered when drilling across naturally or induced fractured formations. This could pose significant operational risks, such as well-control, stuck pipe, and wellbore instability, which, in turn, lead to an increase of well time and cost. This research aims to use and evaluate different machine learning techniques, namely: support vector machines, random forests, and K-nearest neighbors in detecting loss circulation occurrences while drilling using solely drilling surface parameters. Actual field data of seven wells, which had suffered partial or severe loss circulation, were used to build predictive models, while Well-8 was used to compare the performance of the developed models. Different performance metrics were used to evaluate the performance of the developed models. Recall, precision, and F1-score measures were used to evaluate the ability of the developed model to detect loss circulation occurrences. The results showed the K-nearest neighbors classifier achieved a high F1-score of 0.912 in detecting loss circulation occurrence in the testing set, while the random forests was the second-best classifier with almost the same F1-score of 0.910. The support vector machines achieved an F1-score of 0.83 in predicting the loss circulation occurrence in the testing set. The K-nearest neighbors outperformed other models in detecting the loss circulation occurrences in Well-8 with an F1-score of 0.80. The main contribution of this research as compared to previous studies is that it identifies losses events based on real-time measurements of the active pit volume.

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Automated Cobble Mapping of a Mixed Sand-Cobble Beach Using a Mobile LiDAR System

Remote Sensing ◽

10.3390/rs10081253 ◽

2018 ◽

Vol 10 (8) ◽

pp. 1253 ◽

Cited By ~ 4

Author(s):

Hironori Matsumoto ◽

Adam Young

Keyword(s):

Maximum Likelihood ◽

Large Scale ◽

Spatial Location ◽

Machine Learning Techniques ◽

Support Vector ◽

Lidar System ◽

K Nearest Neighbors ◽

Terrestrial Lidar ◽

Topographic Roughness ◽

Cobble Beach

Cobbles (64–256 mm) are found on beaches throughout the world, influence beach morphology, and can provide shoreline stability. Detailed, frequent, and spatially large-scale quantitative cobble observations at beaches are vital toward a better understanding of sand-cobble beach systems. This study used a truck-mounted mobile terrestrial LiDAR system and a raster-based classification approach to map cobbles automatically. Rasters of LiDAR intensity, intensity deviation, topographic roughness, and slope were utilized for cobble classification. Four machine learning techniques including maximum likelihood, decision tree, support vector machine, and k-nearest neighbors were tested on five raster resolutions ranging from 5–50 cm. The cobble mapping capability varied depending on pixel size, classification technique, surface cobble density, and beach setting. The best performer was a maximum likelihood classification using 20 cm raster resolution. Compared to manual mapping at 15 control sites (size ranging from a few to several hundred square meters), automated mapping errors were <12% (best fit line). This method mapped the spatial location of dense cobble regions more accurately compared to sparse and moderate density cobble areas. The method was applied to a ~40 km section of coast in southern California, and successfully generated temporal and spatial cobble distributions consistent with previous observations.

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Feature Selection from Lyme Disease Patient Survey Using Machine Learning

Algorithms ◽

10.3390/a13120334 ◽

2020 ◽

Vol 13 (12) ◽

pp. 334

Author(s):

Joshua Vendrow ◽

Jamie Haddock ◽

Deanna Needell ◽

Lorraine Johnson

Keyword(s):

Machine Learning ◽

Lyme Disease ◽

Large Scale ◽

Disease Patient ◽

Patient Survey ◽

Machine Learning Techniques ◽

Medical Community ◽

Support Vector ◽

Global Rating ◽

K Nearest Neighbors

Lyme disease is a rapidly growing illness that remains poorly understood within the medical community. Critical questions about when and why patients respond to treatment or stay ill, what kinds of treatments are effective, and even how to properly diagnose the disease remain largely unanswered. We investigate these questions by applying machine learning techniques to a large scale Lyme disease patient registry, MyLymeData, developed by the nonprofit LymeDisease.org. We apply various machine learning methods in order to measure the effect of individual features in predicting participants’ answers to the Global Rating of Change (GROC) survey questions that assess the self-reported degree to which their condition improved, worsened, or remained unchanged following antibiotic treatment. We use basic linear regression, support vector machines, neural networks, entropy-based decision tree models, and k-nearest neighbors approaches. We first analyze the general performance of the model and then identify the most important features for predicting participant answers to GROC. After we identify the “key” features, we separate them from the dataset and demonstrate the effectiveness of these features at identifying GROC. In doing so, we highlight possible directions for future study both mathematically and clinically.

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