Ensemble of heterogeneous classifiers for diagnosis and prediction of coronary artery disease with reduced feature subset

Velusamy, Durgadevi; Ramasamy, Karthikeyan

doi:10.1016/j.cmpb.2020.105770

Cited by 76 publications

(46 citation statements)

References 47 publications

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“…A Boruta variable selection method was employed to reduce the number of metrics used in model fitting. The Boruta method for feature selection is popularly used with machine learning applications in genetics, health sciences, and other disciplines where the number of predictor metrics is often much larger than the number of observations [52][53][54]. This method copies each metric and randomly permutes the data creating a set of noise metrics.…”

Section: Area-based Modellingmentioning

confidence: 99%

Use of Multi-Temporal LiDAR to Quantify Fertilization Effects on Stand Volume and Biomass in Late-Rotation Coastal Douglas-Fir Forests

Kelley

Trofymow

Filipescu

et al. 2021

Forests

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Forest fertilization is common in coastal British Columbia as a means to increase wood production and potentially enhance carbon sequestration. Generally, the effects of fertilization are determined by measuring sample plots pre- and post-treatment, resulting in fertilization effects being determined for a limited portion of the treatment area. Applications of remote sensing-based enhanced forest inventories have allowed for estimations to expand to the wider forested area. However, these applications have not focused on monitoring the effects of silvicultural treatments. The objective of this research was to examine if a multi-temporal application of the LiDAR area-based method can be used to detect the fertilization effects on volume, biomass, and height in a second-growth Douglas-fir (Pseudotsuga menziesii) stand. The study area on Vancouver Island was fertilized in January 2007, and sample plots were established in 2011. LiDAR acquisitions were made in 2004, prior to fertilization, and in 2008, 2011, and 2016, covering both treated and untreated areas. A total of 29 paired LiDAR blocks, comprised of four 20 m resolution raster cells, were selected on either side of the fertilization boundary for analysis of the effects across several different stand types differing in the percentage of Douglas-fir, site index, and age. Random forest (RF) plot-level models were developed to estimate total stem volume and total stem biomass for each year of LiDAR acquisition using an area-based approach. Plot level results showed an increase in stem volume by 13% fertilized over control from 2005 to 2011, which was similar to a 14% increase in above-ground carbon stocks estimated using a tree-ring stand reconstruction approach. Plot-level RF models showed R2 values of 0.86 (volume) and 0.92 (biomass) with relative cross-validated root mean square errors of 12.5% (volume) and 11.9% (biomass). For both the sample plots and LiDAR blocks, statistical results indicated no significant differences in volume or biomass between treatments. However, significant differences in height increments were detected between treatments in LiDAR blocks. The results from this research highlight the promising potential for the use of enhanced forest inventory methods to rapidly expand the assessment of treatment effects beyond sample plots to the stand, block, or landscape level.

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Section: Area-based Modellingmentioning

confidence: 99%

Use of Multi-Temporal LiDAR to Quantify Fertilization Effects on Stand Volume and Biomass in Late-Rotation Coastal Douglas-Fir Forests

Kelley

Trofymow

Filipescu

et al. 2021

Forests

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“…As the number of variants was too large to apply deep learning models directly, to construct the features for the deep learning models, we used feature selection to reduce variant dimension (Figure 1B). Feature selection is one of the core concepts in machine learning that hugely impacts the performance of a model [32][33][34][35]. The data features that are used to train machine learning models have a huge influence on the performance that we can achieve.…”

Section: Identifying Contributory Common Genetic Variantsmentioning

confidence: 99%

Diagnostic Classification and Prognostic Prediction Using Common Genetic Variants in Autism Spectrum Disorder: Genotype-Based Deep Learning

Wang¹,

Avillach²

2021

JMIR Med Inform

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Background In the United States, about 3 million people have autism spectrum disorder (ASD), and around 1 out of 59 children are diagnosed with ASD. People with ASD have characteristic social communication deficits and repetitive behaviors. The causes of this disorder remain unknown; however, in up to 25% of cases, a genetic cause can be identified. Detecting ASD as early as possible is desirable because early detection of ASD enables timely interventions in children with ASD. Identification of ASD based on objective pathogenic mutation screening is the major first step toward early intervention and effective treatment of affected children. Objective Recent investigation interrogated genomics data for detecting and treating autism disorders, in addition to the conventional clinical interview as a diagnostic test. Since deep neural networks perform better than shallow machine learning models on complex and high-dimensional data, in this study, we sought to apply deep learning to genetic data obtained across thousands of simplex families at risk for ASD to identify contributory mutations and to create an advanced diagnostic classifier for autism screening. Methods After preprocessing the genomics data from the Simons Simplex Collection, we extracted top ranking common variants that may be protective or pathogenic for autism based on a chi-square test. A convolutional neural network–based diagnostic classifier was then designed using the identified significant common variants to predict autism. The performance was then compared with shallow machine learning–based classifiers and randomly selected common variants. Results The selected contributory common variants were significantly enriched in chromosome X while chromosome Y was also discriminatory in determining the identification of autistic individuals from nonautistic individuals. The ARSD, MAGEB16, and MXRA5 genes had the largest effect in the contributory variants. Thus, screening algorithms were adapted to include these common variants. The deep learning model yielded an area under the receiver operating characteristic curve of 0.955 and an accuracy of 88% for identifying autistic individuals from nonautistic individuals. Our classifier demonstrated a considerable improvement of ~13% in terms of classification accuracy compared to standard autism screening tools. Conclusions Common variants are informative for autism identification. Our findings also suggest that the deep learning process is a reliable method for distinguishing the diseased group from the control group based on the common variants of autism.

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“…In the study of [ 22 ] the obstructive predictive model for coronary artery diseases was developed with machine learning algorithms and result of the study showed that the models perform efficiently. The study of [ 25 ], investigated the ensemble of heterogeneous classifiers for diagnosis of CAD. The authors combined three ML methods: K-nearest neighbour (KNN), random forest (RF) and support vector machine (SVM) for diagnosis of CAD.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Predictive Models for Coronary Artery Disease

et al. 2021

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Coronary artery disease (CAD) is the commonest type of heart disease and over 80% of the deaths resulted from the diseases occurred in developing countries including Nigeria, with majority being in those victims are below 70 years of age. Though, CAD is not a well known disease in Nigeria but however in year 2014, 2.82% of the total of deaths occurred in the country were due to the disease. In this study, a machine leaning predictive models for CAD has been developed with diagnostic CAD dataset obtained in the two General Hospitals in Kano State—Nigeria. The dataset applied on machine learning algorithms which include support vector machine, K nearest neighbor, random tree, Naïve Bayes, gradient boosting and logistic regression algorithms to build the predictive models and the models were evaluated based accuracy, specificity, sensitivity and receiver operating curve (ROC) performance evaluation techniques. In terms of accuracy random forest-based machine learning model emerged to be the best model with 92.04%, for specificity Naive Bayes based machine learning model emerged to be the best model with 92.40%, while for sensitivity support vector machine based machine learning model emerged to be the best model with 87.34% and for ROC, random forest-based machine learning model emerged to be the best model with 92.20%. The decision tree generated with random forest machine learning algorithm which happened to be best model in terms accuracy and ROC can be converted into production rules and be used develop expert system for diagnosis of CAD patients in Nigeria.

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Ensemble of heterogeneous classifiers for diagnosis and prediction of coronary artery disease with reduced feature subset

Cited by 76 publications

References 47 publications

Use of Multi-Temporal LiDAR to Quantify Fertilization Effects on Stand Volume and Biomass in Late-Rotation Coastal Douglas-Fir Forests

Use of Multi-Temporal LiDAR to Quantify Fertilization Effects on Stand Volume and Biomass in Late-Rotation Coastal Douglas-Fir Forests

Diagnostic Classification and Prognostic Prediction Using Common Genetic Variants in Autism Spectrum Disorder: Genotype-Based Deep Learning

Machine Learning Predictive Models for Coronary Artery Disease

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