Coronary Artery Disease Diagnosis; Ranking the Significant Features Using a Random Trees Model

Joloudari, Javad Hassannataj; Joloudari, Edris Hassannataj; Saadatfar, Hamid; GhasemiGol, Mohammad; Razavi, Seyyed Mohammad; Mosavi, Amir; Nabipour, Narjes; Shamshirband, Shahaboddin; Nádai, László

doi:10.3390/ijerph17030731

Cited by 124 publications

(77 citation statements)

References 37 publications

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“…These diseases lead to the death of 300 individuals daily and more than 40% of mortalities based on the statistics provided by the Ministry of Health and Medical Education (MOHME) of Iran and relevant studies. In addition, myocardial infarction is regarded as the cause of more than 19% of mortalities as Iran possesses the maximum mortality rate caused by cardiac arrest in the world [27][28][29][30]. Cardiovascular diseases, stroke, diabetes, dyslipidemia, e.g., are considered as deadly diseases in Iran and today's world.…”

Section: Introductionmentioning

confidence: 99%

The Role of Urban Morphology Design on Enhancing Physical Activity and Public Health

Fathi

Sajadzadeh

Sheshkal

et al. 2020

IJERPH

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Along with environmental pollution, urban planning has been connected to public health. The research indicates that the quality of built environments plays an important role in reducing mental disorders and overall health. The structure and shape of the city are considered as one of the factors influencing happiness and health in urban communities and the type of the daily activities of citizens. The aim of this study was to promote physical activity in the main structure of the city via urban design in a way that the main form and morphology of the city can encourage citizens to move around and have physical activity within the city. Functional, physical, cultural-social, and perceptual-visual features are regarded as the most important and effective criteria in increasing physical activities in urban spaces, based on literature review. The environmental quality of urban spaces and their role in the physical activities of citizens in urban spaces were assessed by using the questionnaire tool and analytical network process (ANP) of structural equation modeling. Further, the space syntax method was utilized to evaluate the role of the spatial integration of urban spaces on improving physical activities. Based on the results, consideration of functional diversity, spatial flexibility and integration, security, and the aesthetic and visual quality of urban spaces plays an important role in improving the physical health of citizens in urban spaces. Further, more physical activities, including motivation for walking and the sense of public health and happiness, were observed in the streets having higher linkage and space syntax indexes with their surrounding texture.

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Section: Introductionmentioning

confidence: 99%

The Role of Urban Morphology Design on Enhancing Physical Activity and Public Health

Fathi

Sajadzadeh

Sheshkal

et al. 2020

IJERPH

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“…In recent years, the medical field has applied data mining to predict the risk of diseases and vulnerable groups [16,17]. Data mining is a type of big data analysis that examines the relationships and rules within a dataset to extract valuable information [18]. The health science field has traditionally used tree-based methods such as Classification and Regression Tree (CART) as data mining methods for disease prediction [19].…”

Section: Introductionmentioning

confidence: 99%

Is the Random Forest Algorithm Suitable for Predicting Parkinson’s Disease with Mild Cognitive Impairment out of Parkinson’s Disease with Normal Cognition?

Byeon

2020

IJERPH

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Because it is possible to delay the progression of dementia if it is detected and treated in an early stage, identifying mild cognitive impairment (MCI) is an important primary goal of dementia treatment. The objectives of this study were to develop a random forest-based Parkinson’s disease with mild cognitive impairment (PD-MCI) prediction model considering health behaviors, environmental factors, medical history, physical functions, depression, and cognitive functions using the Parkinson’s Dementia Clinical Epidemiology Data (a national survey conducted by the Korea Centers for Disease Control and Prevention) and to compare the prediction accuracy of our model with those of decision tree and multiple logistic regression models. We analyzed 96 subjects (PD-MCI = 45; Parkinson’s disease with normal cognition (PD-NC) = 51 subjects). The prediction accuracy of the model was calculated using the overall accuracy, sensitivity, and specificity. Based on the random forest analysis, the major risk factors of PD-MCI were, in descending order of magnitude, Clinical Dementia Rating (CDR) sum of boxes, Untitled Parkinson’s Disease Rating (UPDRS) motor score, the Korean Mini Mental State Examination (K-MMSE) total score, and the K- Korean Montreal Cognitive Assessment (K-MoCA) total score. The random forest method achieved a higher sensitivity than the decision tree model. Thus, it is advisable to develop a protocol to easily identify early stage PDD based on the PD-MCI prediction model developed in this study, in order to establish individualized monitoring to track high-risk groups.

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“…The hybridization of machine learning methods has shown to be an essential approach to improve the performance of the prediction models. For the future research, advancement of hybrid and ensemble machine learning models, e.g., [23][24][25][26][27][28], and comparative analysis with deep learning models, e.g., [29][30][31][32] are proposed to identify models with higher efficiency.…”

Section: Discussionmentioning

confidence: 99%

Performance Analysis of Combine Harvester Using Hybrid Model of Artificial Neural Networks Particle Swarm Optimization

Nádai¹,

Felde²,

Ardabili³

et al. 2020

Preprint

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Novel applications of artificial intelligence for tuning the parameters of industrial machines for optimal performance are emerging at a fast pace. Tuning the combine harvesters and improving the machine performance can dramatically minimize the wastes during harvesting, and it is also beneficial to machine maintenance. Literature includes several soft computing, machine learning and optimization methods that had been used to model the function of harvesters of various crops. Due to the complexity of the problem, machine learning methods had been recently proposed to predict the optimal performance with promising results. In this paper, through proposing a novel hybrid machine learning model based on artificial neural networks integrated with particle swarm optimization (ANN-PSO), the performance analysis of a common combine harvester is presented. The hybridization of machine learning methods with soft computing techniques has recently shown promising results to improve the performance of the combine harvesters. This research aims at improving the results further by providing more stable models with higher accuracy. Keywords-Combine harvester, hybrid machine learning, artificial neural networks (ANN), particle swarm optimization (PSO), ANN-PSO

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Coronary Artery Disease Diagnosis; Ranking the Significant Features Using a Random Trees Model

Cited by 124 publications

References 37 publications

The Role of Urban Morphology Design on Enhancing Physical Activity and Public Health

The Role of Urban Morphology Design on Enhancing Physical Activity and Public Health

Is the Random Forest Algorithm Suitable for Predicting Parkinson’s Disease with Mild Cognitive Impairment out of Parkinson’s Disease with Normal Cognition?

Performance Analysis of Combine Harvester Using Hybrid Model of Artificial Neural Networks Particle Swarm Optimization

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