Ensemble Learning Models Based on Noninvasive Features for Type 2 Diabetes Screening: Model Development and Validation (Preprint)

BACKGROUND Early diabetes screening can effectively reduce the burden of disease. However, natural population–based screening projects require a large number of resources. With the emergence and development of machine learning, researchers have started to pursue more flexible and efficient methods to screen or predict type 2 diabetes. OBJECTIVE The aim of this study was to build prediction models based on the ensemble learning method for diabetes screening to further improve the health status of the population in a noninvasive and inexpensive manner. METHODS The dataset for building and evaluating the diabetes prediction model was extracted from the National Health and Nutrition Examination Survey from 2011-2016. After data cleaning and feature selection, the dataset was split into a training set (80%, 2011-2014), test set (20%, 2011-2014) and validation set (2015-2016). Three simple machine learning methods (linear discriminant analysis, support vector machine, and random forest) and easy ensemble methods were used to build diabetes prediction models. The performance of the models was evaluated through 5-fold cross-validation and external validation. The Delong test (2-sided) was used to test the performance differences between the models. RESULTS We selected 8057 observations and 12 attributes from the database. In the 5-fold cross-validation, the three simple methods yielded highly predictive performance models with areas under the curve (AUCs) over 0.800, wherein the ensemble methods significantly outperformed the simple methods. When we evaluated the models in the test set and validation set, the same trends were observed. The ensemble model of linear discriminant analysis yielded the best performance, with an AUC of 0.849, an accuracy of 0.730, a sensitivity of 0.819, and a specificity of 0.709 in the validation set. CONCLUSIONS This study indicates that efficient screening using machine learning methods with noninvasive tests can be applied to a large population and achieve the objective of secondary prevention.

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Ensemble Learning Models Based on Noninvasive Features for Type 2 Diabetes Screening: Model Development and Validation

JMIR Medical Informatics ◽

10.2196/15431 ◽

2020 ◽

Vol 8 (6) ◽

pp. e15431

Author(s):

Tianzhou Yang ◽

Li Zhang ◽

Liwei Yi ◽

Huawei Feng ◽

Shimeng Li ◽

...

Keyword(s):

Machine Learning ◽

Prediction Models ◽

Ensemble Methods ◽

Diabetes Screening ◽

Linear Discriminant ◽

Machine Learning Methods ◽

Diabetes Prediction ◽

Validation Set ◽

Fold Cross Validation

Background Early diabetes screening can effectively reduce the burden of disease. However, natural population–based screening projects require a large number of resources. With the emergence and development of machine learning, researchers have started to pursue more flexible and efficient methods to screen or predict type 2 diabetes. Objective The aim of this study was to build prediction models based on the ensemble learning method for diabetes screening to further improve the health status of the population in a noninvasive and inexpensive manner. Methods The dataset for building and evaluating the diabetes prediction model was extracted from the National Health and Nutrition Examination Survey from 2011-2016. After data cleaning and feature selection, the dataset was split into a training set (80%, 2011-2014), test set (20%, 2011-2014) and validation set (2015-2016). Three simple machine learning methods (linear discriminant analysis, support vector machine, and random forest) and easy ensemble methods were used to build diabetes prediction models. The performance of the models was evaluated through 5-fold cross-validation and external validation. The Delong test (2-sided) was used to test the performance differences between the models. Results We selected 8057 observations and 12 attributes from the database. In the 5-fold cross-validation, the three simple methods yielded highly predictive performance models with areas under the curve (AUCs) over 0.800, wherein the ensemble methods significantly outperformed the simple methods. When we evaluated the models in the test set and validation set, the same trends were observed. The ensemble model of linear discriminant analysis yielded the best performance, with an AUC of 0.849, an accuracy of 0.730, a sensitivity of 0.819, and a specificity of 0.709 in the validation set. Conclusions This study indicates that efficient screening using machine learning methods with noninvasive tests can be applied to a large population and achieve the objective of secondary prevention.

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Machine Learning Methods for Diabetes Prediction

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.l2973.1081219 ◽

2019 ◽

Vol 8 (12) ◽

pp. 2199-2205

Keyword(s):

Machine Learning ◽

Risk Factor ◽

Research Field ◽

Support Vector ◽

Machine Learning Methods ◽

Health Practitioners ◽

Prediction Technique ◽

Diabetes Prediction ◽

Machine Learning Models

Machine Learning is one of the methods used for task prediction. In the diabetic’s research field, the application of machine learning is emerging since the advantages of approximation on the prediction technique has significantly given insight for many health practitioners. Machine Learning is utilized in order to handle the uncontrollable risk factor by finding a relation between such a risk factor trough prediction. This study aims to review recent machine learning models that have been used in diabetes prediction with respect to the risk factors in order to prevent diabetes. This study compares the performance of the model by justified the accuracy as the baseline to evaluate the model. The result of this review shows that the Random Forest and Support Vector Machine are the most popular technique among researcher. Moreover, from this study, it can be seen that Type 2 Diabetes Mellitus (T2DM) has been a concern by researchers since the incidence of diabetes was increasing in worldwide today that happened from an uncontrollable risk factor

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Machine Learning Methods Applied to the Prediction of Pseudo-nitzschia spp. Blooms in the Galician Rias Baixas (NW Spain)

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10040199 ◽

2021 ◽

Vol 10 (4) ◽

pp. 199

Author(s):

Francisco M. Bellas Aláez ◽

Jesus M. Torres Palenzuela ◽

Evangelos Spyrakos ◽

Luis González Vilas

Keyword(s):

Machine Learning ◽

Performance Metrics ◽

Prediction Models ◽

Support Vector ◽

False Alarms ◽

Learning Approaches ◽

Learning Methods ◽

Machine Learning Methods ◽

Rías Baixas ◽

New Algorithms

This work presents new prediction models based on recent developments in machine learning methods, such as Random Forest (RF) and AdaBoost, and compares them with more classical approaches, i.e., support vector machines (SVMs) and neural networks (NNs). The models predict Pseudo-nitzschia spp. blooms in the Galician Rias Baixas. This work builds on a previous study by the authors (doi.org/10.1016/j.pocean.2014.03.003) but uses an extended database (from 2002 to 2012) and new algorithms. Our results show that RF and AdaBoost provide better prediction results compared to SVMs and NNs, as they show improved performance metrics and a better balance between sensitivity and specificity. Classical machine learning approaches show higher sensitivities, but at a cost of lower specificity and higher percentages of false alarms (lower precision). These results seem to indicate a greater adaptation of new algorithms (RF and AdaBoost) to unbalanced datasets. Our models could be operationally implemented to establish a short-term prediction system.

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Machine Learning in Aging: An Example of Developing Prediction Models for Serious Fall Injury in Older Adults

Innovation in Aging ◽

10.1093/geroni/igaa057.859 ◽

2020 ◽

Vol 4 (Supplement_1) ◽

pp. 268-269

Author(s):

Jaime Speiser ◽

Kathryn Callahan ◽

Jason Fanning ◽

Thomas Gill ◽

Anne Newman ◽

...

Keyword(s):

Machine Learning ◽

Older Adults ◽

Random Forest ◽

Decision Tree ◽

Prediction Models ◽

Receiver Operating Curve ◽

Learning Methods ◽

Life Study ◽

Fall Injury ◽

Machine Learning Methods

Abstract Advances in computational algorithms and the availability of large datasets with clinically relevant characteristics provide an opportunity to develop machine learning prediction models to aid in diagnosis, prognosis, and treatment of older adults. Some studies have employed machine learning methods for prediction modeling, but skepticism of these methods remains due to lack of reproducibility and difficulty understanding the complex algorithms behind models. We aim to provide an overview of two common machine learning methods: decision tree and random forest. We focus on these methods because they provide a high degree of interpretability. We discuss the underlying algorithms of decision tree and random forest methods and present a tutorial for developing prediction models for serious fall injury using data from the Lifestyle Interventions and Independence for Elders (LIFE) study. Decision tree is a machine learning method that produces a model resembling a flow chart. Random forest consists of a collection of many decision trees whose results are aggregated. In the tutorial example, we discuss evaluation metrics and interpretation for these models. Illustrated in data from the LIFE study, prediction models for serious fall injury were moderate at best (area under the receiver operating curve of 0.54 for decision tree and 0.66 for random forest). Machine learning methods may offer improved performance compared to traditional models for modeling outcomes in aging, but their use should be justified and output should be carefully described. Models should be assessed by clinical experts to ensure compatibility with clinical practice.

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Advances in Blast-Induced Impact Prediction—A Review of Machine Learning Applications

Minerals ◽

10.3390/min11060601 ◽

2021 ◽

Vol 11 (6) ◽

pp. 601

Author(s):

Nelson K. Dumakor-Dupey ◽

Sampurna Arya ◽

Ankit Jha

Keyword(s):

Machine Learning ◽

Prediction Models ◽

Academic Research ◽

Empirical Models ◽

Rock Breakage ◽

Environmental Implications ◽

Learning Methods ◽

Factors Affecting ◽

Impact Prediction ◽

Machine Learning Methods

Rock fragmentation in mining and construction industries is widely achieved using drilling and blasting technique. The technique remains the most effective and efficient means of breaking down rock mass into smaller pieces. However, apart from its intended purpose of rock breakage, throw, and heave, blasting operations generate adverse impacts, such as ground vibration, airblast, flyrock, fumes, and noise, that have significant operational and environmental implications on mining activities. Consequently, blast impact studies are conducted to determine an optimum blast design that can maximize the desirable impacts and minimize the undesirable ones. To achieve this objective, several blast impact estimation empirical models have been developed. However, despite being the industry benchmark, empirical model results are based on a limited number of factors affecting the outcomes of a blast. As a result, modern-day researchers are employing machine learning (ML) techniques for blast impact prediction. The ML approach can incorporate several factors affecting the outcomes of a blast, and therefore, it is preferred over empirical and other statistical methods. This paper reviews the various blast impacts and their prediction models with a focus on empirical and machine learning methods. The details of the prediction methods for various blast impacts—including their applications, advantages, and limitations—are discussed. The literature reveals that the machine learning methods are better predictors compared to the empirical models. However, we observed that presently these ML models are mainly applied in academic research.

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Analysis of EPID Transmission Fluence Maps Using Machine Learning Models and CNN for Identifying Position Errors in the Treatment of GO Patients

Frontiers in Oncology ◽

10.3389/fonc.2021.721591 ◽

2021 ◽

Vol 11 ◽

Author(s):

Guyu Dai ◽

Xiangbin Zhang ◽

Wenjie Liu ◽

Zhibin Li ◽

Guangyu Wang ◽

...

Keyword(s):

Machine Learning ◽

Error Type ◽

Imaging Device ◽

Linear Discriminant ◽

Position Errors ◽

Dose Monitoring ◽

Type 3

PurposeTo find a suitable method for analyzing electronic portal imaging device (EPID) transmission fluence maps for the identification of position errors in the in vivo dose monitoring of patients with Graves’ ophthalmopathy (GO).MethodsPosition errors combining 0-, 2-, and 4-mm errors in the left-right (LR), anterior-posterior (AP), and superior-inferior (SI) directions in the delivery of 40 GO patient radiotherapy plans to a human head phantom were simulated and EPID transmission fluence maps were acquired. Dose difference (DD) and structural similarity (SSIM) maps were calculated to quantify changes in the fluence maps. Three types of machine learning (ML) models that utilize radiomics features of the DD maps (ML 1 models), features of the SSIM maps (ML 2 models), and features of both DD and SSIM maps (ML 3 models) as inputs were used to perform three types of position error classification, namely a binary classification of the isocenter error (type 1), three binary classifications of LR, SI, and AP direction errors (type 2), and an eight-element classification of the combined LR, SI, and AP direction errors (type 3). Convolutional neural network (CNN) was also used to classify position errors using the DD and SSIM maps as input.ResultsThe best-performing ML 1 model was XGBoost, which achieved accuracies of 0.889, 0.755, 0.778, 0.833, and 0.532 in the type 1, type 2-LR, type 2-AP, type 2-SI, and type 3 classification, respectively. The best ML 2 model was XGBoost, which achieved accuracies of 0.856, 0.731, 0.736, 0.949, and 0.491, respectively. The best ML 3 model was linear discriminant classifier (LDC), which achieved accuracies of 0.903, 0.792, 0.870, 0.931, and 0.671, respectively. The CNN achieved classification accuracies of 0.925, 0.833, 0.875, 0.949, and 0.689, respectively.ConclusionML models and CNN using combined DD and SSIM maps can analyze EPID transmission fluence maps to identify position errors in the treatment of GO patients. Further studies with large sample sizes are needed to improve the accuracy of CNN.

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An Activity-Aware Sampling Scheme for Mobile Phones in Activity Recognition

Sensors ◽

10.3390/s20082189 ◽

2020 ◽

Vol 20 (8) ◽

pp. 2189

Author(s):

Zhimin Chen ◽

Jianxin Chen ◽

Xiangjun Huang

Keyword(s):

Machine Learning ◽

Energy Efficiency ◽

Activity Recognition ◽

Mobile Phones ◽

Recognition Accuracy ◽

Sampling Rate ◽

University Of California ◽

Linear Discriminant ◽

Improve Energy Efficiency ◽

Machine Learning Methods

In recent years, sensors in smartphones have been widely used in applications, e.g., human activity recognition (HAR). However, the power of smartphone constrains the applications of HAR due to the computations. To combat it, energy efficiency should be considered in the applications of HAR with smartphones. In this paper, we improve energy efficiency for smartphones by adaptively controlling the sampling rate of the sensors during HAR. We collect the sensor samples, depending on the activity changing, based on the magnitude of acceleration. Besides that, we use linear discriminant analysis (LDA) to select the feature and machine learning methods for activity classification. Our method is verified on the UCI (University of California, Irvine) dataset; and it achieves an overall 56.39% of energy saving and the recognition accuracy of 99.58% during the HAR applications with smartphone.

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The comparison of machine learning methods for prediction study of type 2 diabetes mellitus’s drug design

10.1063/5.0024161 ◽

2020 ◽

Author(s):

Nadya Asanul Husna ◽

Alhadi Bustamam ◽

Arry Yanuar ◽

Devvi Sarwinda ◽

Oky Hermansyah

Keyword(s):

Machine Learning ◽

Type 2 Diabetes ◽

Drug Design ◽

Learning Methods ◽

Machine Learning Methods

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Classification models using circulating neutrophil transcripts can detect unruptured intracranial aneurysm

Journal of Translational Medicine ◽

10.1186/s12967-020-02550-2 ◽

2020 ◽

Vol 18 (1) ◽

Author(s):

Kerry E. Poppenberg ◽

Vincent M. Tutino ◽

Lu Li ◽

Muhammad Waqas ◽

Armond June ◽

...

Keyword(s):

Machine Learning ◽

Random Forest ◽

Prediction Models ◽

Model Performance ◽

Supervised Machine Learning ◽

Support Vector ◽

Learning Methods ◽

Training Cohort ◽

Network Analyses ◽

Machine Learning Methods

Abstract Background Intracranial aneurysms (IAs) are dangerous because of their potential to rupture. We previously found significant RNA expression differences in circulating neutrophils between patients with and without unruptured IAs and trained machine learning models to predict presence of IA using 40 neutrophil transcriptomes. Here, we aim to develop a predictive model for unruptured IA using neutrophil transcriptomes from a larger population and more robust machine learning methods. Methods Neutrophil RNA extracted from the blood of 134 patients (55 with IA, 79 IA-free controls) was subjected to next-generation RNA sequencing. In a randomly-selected training cohort (n = 94), the Least Absolute Shrinkage and Selection Operator (LASSO) selected transcripts, from which we constructed prediction models via 4 well-established supervised machine-learning algorithms (K-Nearest Neighbors, Random Forest, and Support Vector Machines with Gaussian and cubic kernels). We tested the models in the remaining samples (n = 40) and assessed model performance by receiver-operating-characteristic (ROC) curves. Real-time quantitative polymerase chain reaction (RT-qPCR) of 9 IA-associated genes was used to verify gene expression in a subset of 49 neutrophil RNA samples. We also examined the potential influence of demographics and comorbidities on model prediction. Results Feature selection using LASSO in the training cohort identified 37 IA-associated transcripts. Models trained using these transcripts had a maximum accuracy of 90% in the testing cohort. The testing performance across all methods had an average area under ROC curve (AUC) = 0.97, an improvement over our previous models. The Random Forest model performed best across both training and testing cohorts. RT-qPCR confirmed expression differences in 7 of 9 genes tested. Gene ontology and IPA network analyses performed on the 37 model genes reflected dysregulated inflammation, cell signaling, and apoptosis processes. In our data, demographics and comorbidities did not affect model performance. Conclusions We improved upon our previous IA prediction models based on circulating neutrophil transcriptomes by increasing sample size and by implementing LASSO and more robust machine learning methods. Future studies are needed to validate these models in larger cohorts and further investigate effect of covariates.

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