Special Issue "Disease Prediction, Machine Learning, and Healthcare"

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Health Care Sciences & Services".

Deadline for manuscript submissions: 31 December 2021.

Special Issue Editor

Prof. Dr. Keun Ho Ryu
E-Mail Website
Guest Editor
1. Department of Computer Science, Chungbuk National University, Cheongju 28644, Korea
2. Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam
3. Biomedical Engineering Institute, Chiang Mai University, Chiang Mai 50200, Thailand
Interests: big data and databases; data mining; biomedical informatics; and bioinformatics; deep learning and interdisciplinary applications
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

The goal of this Special Issue is to explore how emerging technology solutions and systems in disease and healthcare applications can help human beings to lead heathy lives. Specifically, innovative contributions that either solve or advance the understanding of issues related to new technologies and applications in the real world are very welcome.

This Special Issue also seeks to not only bring solutions that combine state-of-the-art prediction methods for exploiting the huge health and bio data resources available (while ensuring that these systems are explainable to domain experts), but also emerging methods that more generally describe the successful application of AI and big data analytic methodologies to issues such as disease prediction, machine learning, deep learning, knowledge discovery, big data, and feature selection in the medical domain as well as healthcare, biology, and wellbeing domains. The main idea is to cover health data analytics issues addressing all facets of the solutions from the disease prediction and healthcare technology perspective.

The general idea behind this Special Issue is to disseminate disease prediction and healthcare solution contributions from various engineering, scientific, and social settings that exploit data analytics, machine learning, and data mining techniques.

This Special Issue will include papers that span a wide range of topics in the fields of applied medical informatics, healthcare, bioinformatics, and data analytics, ranging from methodological aspects to theoretical and technological views. More specifically, this Special Issue covers some emerging and real-world applicable research topics concerning new trends in applied data analytics, such as machine learning, deep learning, knowledge discovery, feature selection, data analytics, big data platform-related disease prediction and healthcare, and medical data analytics.

A variety of modern real-life settings along with academic and industrial contexts could benefit from the dissemination of these advances and novel paradigms covering all facets of the data discovery process. Industries and modern applications could share their experience in exploiting medical and healthcare solutions keeping pace with the latest technologies. Academics could identify open research issues coming from the industrial and real-life contexts to continuously support the methodological and technological solutions.

TOPICS OF INTEREST

This Special Issue welcomes the submission of technical, experimental, methodological, and data analytical contributions focused on real-world problems and systems, as well as on general applications of AI and big data analytic methodologies in medical Informatics, bioinformatics, medical and health data, and healthcare applications, including but not limited to the following topics:

        - Disease prediction methods and techniques;
        - Data mining and knowledge discovery in healthcare;
        - Machine and deep learning approaches for disease and health data;
        - Decision support systems for healthcare and wellbeing;
        - Optimization for healthcare problems;
        - Regression and forecasting for medical and/or biomedical signals;
        - Healthcare information systems;
        - Wellness information systems;
        - Medical signal and image processing and techniques;
        - Medical expert systems;
        - Biomedical applications;
        - Applications of AI techniques in healthcare and wellbeing systems;
        - Machine learning-based medical systems;
        - Medical data and knowledge bases;
        - Neural networks in medical applications;
        - Intelligent computing and platforms in medicine and healthcare;
        - Biomedical text mining;
        - Deep learning and methods to explain disease prediction;
        - Big data frameworks and architectures for applied medical and health data;
      - Visualization and interactive interfaces related to healthcare systems.

Dr. Keun Ho Ryu
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Environmental Research and Public Health is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2300 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • disease prediction
  • machine learning
  • deep learning
  • big data
  • data analytics
  • medical and health data
  • healthcare

Published Papers (12 papers)

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Research

Article
Facebook Reviews as a Supplemental Tool for Hospital Patient Satisfaction and Its Relationship with Hospital Accreditation in Malaysia
Int. J. Environ. Res. Public Health 2021, 18(14), 7454; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18147454 - 13 Jul 2021
Viewed by 311
Abstract
Patient satisfaction is one indicator used to assess the impact of accreditation on patient care. However, traditional patient satisfaction surveys have a few disadvantages, and some researchers have suggested that social media be used in their place. Social media usage is gaining popularity [...] Read more.
Patient satisfaction is one indicator used to assess the impact of accreditation on patient care. However, traditional patient satisfaction surveys have a few disadvantages, and some researchers have suggested that social media be used in their place. Social media usage is gaining popularity in healthcare organizations, but there is still a paucity of data to support it. The purpose of this study was to determine the association between online reviews and hospital patient satisfaction and the relationship between online reviews and hospital accreditation. We used a cross-sectional design with data acquired from the official Facebook pages of 48 Malaysian public hospitals, 25 of which are accredited. We collected all patient comments from Facebook reviews of those hospitals between 2018 and 2019. Spearman’s correlation and logistic regression were used to evaluate the data. There was a significant and moderate correlation between hospital patient satisfaction and online reviews. Patient satisfaction was closely connected to urban location, tertiary hospital, and previous Facebook ratings. However, hospital accreditation was not found to be significantly associated with online reports of patient satisfaction. This groundbreaking study demonstrates how Facebook reviews can assist hospital administrators in monitoring their institutions’ quality of care in real time. Full article
(This article belongs to the Special Issue Disease Prediction, Machine Learning, and Healthcare)
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Article
Predicting Survival in Veterans with Follicular Lymphoma Using Structured Electronic Health Record Information and Machine Learning
Int. J. Environ. Res. Public Health 2021, 18(5), 2679; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18052679 - 07 Mar 2021
Viewed by 702
Abstract
The most accurate prognostic approach for follicular lymphoma (FL), progression of disease at 24 months (POD24), requires two years’ observation after initiating first-line therapy (L1) to predict outcomes. We applied machine learning to structured electronic health record (EHR) data to predict individual survival [...] Read more.
The most accurate prognostic approach for follicular lymphoma (FL), progression of disease at 24 months (POD24), requires two years’ observation after initiating first-line therapy (L1) to predict outcomes. We applied machine learning to structured electronic health record (EHR) data to predict individual survival at L1 initiation. We grouped 523 observations and 1933 variables from a nationwide cohort of FL patients diagnosed 2006–2014 in the Veterans Health Administration into traditionally used prognostic variables (“curated”), commonly measured labs (“labs”), and International Classification of Diseases diagnostic codes (“ICD”) sets. We compared performance of random survival forests (RSF) vs. traditional Cox model using four datasets: curated, curated + labs, curated + ICD, and curated + ICD + labs, also using Cox on curated + POD24. We evaluated variable importance and partial dependence plots with area under the receiver operating characteristic curve (AUC). RSF with curated + labs performed best, with mean AUC 0.73 (95% CI: 0.71–0.75). It approximated, but did not surpass, Cox with POD24 (mean AUC 0.74 [95% CI: 0.71–0.77]). RSF using EHR data achieved better performance than traditional prognostic variables, setting the foundation for the incorporation of our algorithm into the EHR. It also provides for possible future scenarios in which clinicians could be provided an EHR-based tool which approximates the predictive ability of the most accurate known indicator, using information available 24 months earlier. Full article
(This article belongs to the Special Issue Disease Prediction, Machine Learning, and Healthcare)
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Article
A Deep Learning Model for Classification of Endoscopic Gastroesophageal Reflux Disease
Int. J. Environ. Res. Public Health 2021, 18(5), 2428; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18052428 - 02 Mar 2021
Viewed by 807
Abstract
Gastroesophageal reflux disease (GERD) is a common disease with high prevalence, and its endoscopic severity can be evaluated using the Los Angeles classification (LA grade). This paper proposes a deep learning model (i.e., GERD-VGGNet) that employs convolutional neural networks for automatic classification and [...] Read more.
Gastroesophageal reflux disease (GERD) is a common disease with high prevalence, and its endoscopic severity can be evaluated using the Los Angeles classification (LA grade). This paper proposes a deep learning model (i.e., GERD-VGGNet) that employs convolutional neural networks for automatic classification and interpretation of routine GERD LA grade. The proposed model employs a data augmentation technique, a two-stage no-freezing fine-tuning policy, and an early stopping criterion. As a result, the proposed model exhibits high generalizability. A dataset of images from 464 patients was used for model training and validation. An additional 32 patients served as a test set to evaluate the accuracy of both the model and our trainees. Experimental results demonstrate that the best model for the development set exhibited an overall accuracy of 99.2% (grade A–B), 100% (grade C–D), and 100% (normal group) using narrow-band image (NBI) endoscopy. On the test set, the proposed model resulted in an accuracy of 87.9%, which was significantly higher than the results of the trainees (75.0% and 65.6%). The proposed GERD-VGGNet model can assist automatic classification of GERD in conventional and NBI environments and thereby increase the accuracy of interpretation of the results by inexperienced endoscopists. Full article
(This article belongs to the Special Issue Disease Prediction, Machine Learning, and Healthcare)
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Article
Deep Learning Feature Extraction Approach for Hematopoietic Cancer Subtype Classification
Int. J. Environ. Res. Public Health 2021, 18(4), 2197; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18042197 - 23 Feb 2021
Viewed by 900
Abstract
Hematopoietic cancer is a malignant transformation in immune system cells. Hematopoietic cancer is characterized by the cells that are expressed, so it is usually difficult to distinguish its heterogeneities in the hematopoiesis process. Traditional approaches for cancer subtyping use statistical techniques. Furthermore, due [...] Read more.
Hematopoietic cancer is a malignant transformation in immune system cells. Hematopoietic cancer is characterized by the cells that are expressed, so it is usually difficult to distinguish its heterogeneities in the hematopoiesis process. Traditional approaches for cancer subtyping use statistical techniques. Furthermore, due to the overfitting problem of small samples, in case of a minor cancer, it does not have enough sample material for building a classification model. Therefore, we propose not only to build a classification model for five major subtypes using two kinds of losses, namely reconstruction loss and classification loss, but also to extract suitable features using a deep autoencoder. Furthermore, for considering the data imbalance problem, we apply an oversampling algorithm, the synthetic minority oversampling technique (SMOTE). For validation of our proposed autoencoder-based feature extraction approach for hematopoietic cancer subtype classification, we compared other traditional feature selection algorithms (principal component analysis, non-negative matrix factorization) and classification algorithms with the SMOTE oversampling approach. Additionally, we used the Shapley Additive exPlanations (SHAP) interpretation technique in our model to explain the important gene/protein for hematopoietic cancer subtype classification. Furthermore, we compared five widely used classification algorithms, including logistic regression, random forest, k-nearest neighbor, artificial neural network and support vector machine. The results of autoencoder-based feature extraction approaches showed good performance, and the best result was the SMOTE oversampling-applied support vector machine algorithm consider both focal loss and reconstruction loss as the loss function for autoencoder (AE) feature selection approach, which produced 97.01% accuracy, 92.60% recall, 99.52% specificity, 93.54% F1-measure, 97.87% G-mean and 95.46% index of balanced accuracy as subtype classification performance measures. Full article
(This article belongs to the Special Issue Disease Prediction, Machine Learning, and Healthcare)
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Article
Combinatorial K-Means Clustering as a Machine Learning Tool Applied to Diabetes Mellitus Type 2
Int. J. Environ. Res. Public Health 2021, 18(4), 1919; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18041919 - 17 Feb 2021
Cited by 1 | Viewed by 1068
Abstract
A new original procedure based on k-means clustering is designed to find the most appropriate clinical variables able to efficiently separate into groups similar patients diagnosed with diabetes mellitus type 2 (DMT2) and underlying diseases (arterial hypertonia (AH), ischemic heart disease (CHD), diabetic [...] Read more.
A new original procedure based on k-means clustering is designed to find the most appropriate clinical variables able to efficiently separate into groups similar patients diagnosed with diabetes mellitus type 2 (DMT2) and underlying diseases (arterial hypertonia (AH), ischemic heart disease (CHD), diabetic polyneuropathy (DPNP), and diabetic microangiopathy (DMA)). Clustering is a machine learning tool for discovering structures in datasets. Clustering has been proven to be efficient for pattern recognition based on clinical records. The considered combinatorial k-means procedure explores all possible k-means clustering with a determined number of descriptors and groups. The predetermined conditions for the partitioning were as follows: every single group of patients included patients with DMT2 and one of the underlying diseases; each subgroup formed in such a way was subject to partitioning into three patterns (good health status, medium health status, and degenerated health status); optimal descriptors for each disease and groups. The selection of the best clustering is obtained through the parameter called global variance, defined as the sum of all variance values of all clinical variables of all the clusters. The best clinical parameters are found by minimizing this global variance. This methodology has to identify a set of variables that are assumed to separate each underlying disease efficiently in three different subgroups of patients. The hierarchical clustering obtained for these four underlying diseases could be used to build groups of patients with correlated clinical data. The proposed methodology gives surmised results from complex data based on a relationship with the health status of the group and draws a picture of the prediction rate of the ongoing health status. Full article
(This article belongs to the Special Issue Disease Prediction, Machine Learning, and Healthcare)
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Article
Predictors of Contemporary under-5 Child Mortality in Low- and Middle-Income Countries: A Machine Learning Approach
Int. J. Environ. Res. Public Health 2021, 18(3), 1315; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18031315 - 01 Feb 2021
Cited by 1 | Viewed by 919
Abstract
Child Mortality (CM) is a worldwide concern, annually affecting as many as 6.81% children in low- and middle-income countries (LMIC). We used data of the Multiple Indicators Cluster Survey (MICS) (N = 275,160) from 27 LMIC and a machine-learning approach to rank 37 [...] Read more.
Child Mortality (CM) is a worldwide concern, annually affecting as many as 6.81% children in low- and middle-income countries (LMIC). We used data of the Multiple Indicators Cluster Survey (MICS) (N = 275,160) from 27 LMIC and a machine-learning approach to rank 37 distal causes of CM and identify the top 10 causes in terms of predictive potency. Based on the top 10 causes, we identified households with improved conditions. We retrospectively validated the results by investigating the association between variations of CM and variations of the percentage of households with improved conditions at country-level, between the 2005–2007 and the 2013–2017 administrations of the MICS. A unique contribution of our approach is to identify lesser-known distal causes which likely account for better-known proximal causes: notably, the identified distal causes and preventable and treatable through social, educational, and physical interventions. We demonstrate how machine learning can be used to obtain operational information from big dataset to guide interventions and policy makers. Full article
(This article belongs to the Special Issue Disease Prediction, Machine Learning, and Healthcare)
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Article
Feasibility of Using Floor Vibration to Detect Human Falls
Int. J. Environ. Res. Public Health 2021, 18(1), 200; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18010200 - 29 Dec 2020
Cited by 2 | Viewed by 550
Abstract
With the increasing aging population in modern society, falls as well as fall-induced injuries in elderly people become one of the major public health problems. This study proposes a classification framework that uses floor vibrations to detect fall events as well as distinguish [...] Read more.
With the increasing aging population in modern society, falls as well as fall-induced injuries in elderly people become one of the major public health problems. This study proposes a classification framework that uses floor vibrations to detect fall events as well as distinguish different fall postures. A scaled 3D-printed model with twelve fully adjustable joints that can simulate human body movement was built to generate human fall data. The mass proportion of a human body takes was carefully studied and was reflected in the model. Object drops, human falling tests were carried out and the vibration signature generated in the floor was recorded for analyses. Machine learning algorithms including K-means algorithm and K nearest neighbor algorithm were introduced in the classification process. Three classifiers (human walking versus human fall, human fall versus object drop, human falls from different postures) were developed in this study. Results showed that the three proposed classifiers can achieve the accuracy of 100, 85, and 91%. This paper developed a framework of using floor vibration to build the pattern recognition system in detecting human falls based on a machine learning approach. Full article
(This article belongs to the Special Issue Disease Prediction, Machine Learning, and Healthcare)
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Article
Screening Model for Estimating Undiagnosed Diabetes among People with a Family History of Diabetes Mellitus: A KNHANES-Based Study
Int. J. Environ. Res. Public Health 2020, 17(23), 8903; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17238903 - 30 Nov 2020
Cited by 3 | Viewed by 879
Abstract
A screening model for estimating undiagnosed diabetes mellitus (UDM) is important for early medical care. There is minimal research and a serious lack of screening models for people with a family history of diabetes (FHD), especially one which incorporates gender characteristics. Therefore, the [...] Read more.
A screening model for estimating undiagnosed diabetes mellitus (UDM) is important for early medical care. There is minimal research and a serious lack of screening models for people with a family history of diabetes (FHD), especially one which incorporates gender characteristics. Therefore, the primary objective of our study was to develop a screening model for estimating UDM among people with FHD and enable its validation. We used data from the Korean National Health and Nutrition Examination Survey (KNHANES). KNAHNES (2010–2016) was used as a developmental cohort (n = 5939) and was then evaluated in a validation cohort (n = 1047) KNHANES (2017). We developed the screening model for UDM in male (SMM), female (SMF), and male and female combined (SMP) with FHD using backward stepwise logistic regression analysis. The SMM and SMF showed an appropriate performance (area under curve (AUC) = 76.2% and 77.9%) compared with SMP (AUC = 72.9%) in the validation cohort. Consequently, simple screening models were developed and validated, for the estimation of UDM among patients in the FHD group, which is expected to reduce the burden on the national health care system. Full article
(This article belongs to the Special Issue Disease Prediction, Machine Learning, and Healthcare)
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Article
Coronary Artery Disease Diagnosis; Ranking the Significant Features Using a Random Trees Model
Int. J. Environ. Res. Public Health 2020, 17(3), 731; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17030731 - 23 Jan 2020
Cited by 19 | Viewed by 1719
Abstract
Heart disease is one of the most common diseases in middle-aged citizens. Among the vast number of heart diseases, coronary artery disease (CAD) is considered as a common cardiovascular disease with a high death rate. The most popular tool for diagnosing CAD is [...] Read more.
Heart disease is one of the most common diseases in middle-aged citizens. Among the vast number of heart diseases, coronary artery disease (CAD) is considered as a common cardiovascular disease with a high death rate. The most popular tool for diagnosing CAD is the use of medical imaging, e.g., angiography. However, angiography is known for being costly and also associated with a number of side effects. Hence, the purpose of this study is to increase the accuracy of coronary heart disease diagnosis through selecting significant predictive features in order of their ranking. In this study, we propose an integrated method using machine learning. The machine learning methods of random trees (RTs), decision tree of C5.0, support vector machine (SVM), and decision tree of Chi-squared automatic interaction detection (CHAID) are used in this study. The proposed method shows promising results and the study confirms that the RTs model outperforms other models. Full article
(This article belongs to the Special Issue Disease Prediction, Machine Learning, and Healthcare)
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Article
Ontology-Based Healthcare Named Entity Recognition from Twitter Messages Using a Recurrent Neural Network Approach
Int. J. Environ. Res. Public Health 2019, 16(19), 3628; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph16193628 - 27 Sep 2019
Cited by 7 | Viewed by 1570
Abstract
Named Entity Recognition (NER) in the healthcare domain involves identifying and categorizing disease, drugs, and symptoms for biosurveillance, extracting their related properties and activities, and identifying adverse drug events appearing in texts. These tasks are important challenges in healthcare. Analyzing user messages in [...] Read more.
Named Entity Recognition (NER) in the healthcare domain involves identifying and categorizing disease, drugs, and symptoms for biosurveillance, extracting their related properties and activities, and identifying adverse drug events appearing in texts. These tasks are important challenges in healthcare. Analyzing user messages in social media networks such as Twitter can provide opportunities to detect and manage public health events. Twitter provides a broad range of short messages that contain interesting information for information extraction. In this paper, we present a Health-Related Named Entity Recognition (HNER) task using healthcare-domain ontology that can recognize health-related entities from large numbers of user messages from Twitter. For this task, we employ a deep learning architecture which is based on a recurrent neural network (RNN) with little feature engineering. To achieve our goal, we collected a large number of Twitter messages containing health-related information, and detected biomedical entities from the Unified Medical Language System (UMLS). A bidirectional long short-term memory (BiLSTM) model learned rich context information, and a convolutional neural network (CNN) was used to produce character-level features. The conditional random field (CRF) model predicted a sequence of labels that corresponded to a sequence of inputs, and the Viterbi algorithm was used to detect health-related entities from Twitter messages. We provide comprehensive results giving valuable insights for identifying medical entities in Twitter for various applications. The BiLSTM-CRF model achieved a precision of 93.99%, recall of 73.31%, and F1-score of 81.77% for disease or syndrome HNER; a precision of 90.83%, recall of 81.98%, and F1-score of 87.52% for sign or symptom HNER; and a precision of 94.85%, recall of 73.47%, and F1-score of 84.51% for pharmacologic substance named entities. The ontology-based manual annotation results show that it is possible to perform high-quality annotation despite the complexity of medical terminology and the lack of context in tweets. Full article
(This article belongs to the Special Issue Disease Prediction, Machine Learning, and Healthcare)
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Article
Taming Performance Variability of Healthcare Data Service Frameworks with Proactive and Coarse-Grained Memory Cleaning
Int. J. Environ. Res. Public Health 2019, 16(17), 3096; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph16173096 - 26 Aug 2019
Viewed by 955
Abstract
This article explores the performance optimizations of an embedded database memory management system to ensure high responsiveness of real-time healthcare data frameworks. SQLite is a popular embedded database engine extensively used in medical and healthcare data storage systems. However, SQLite is essentially built [...] Read more.
This article explores the performance optimizations of an embedded database memory management system to ensure high responsiveness of real-time healthcare data frameworks. SQLite is a popular embedded database engine extensively used in medical and healthcare data storage systems. However, SQLite is essentially built around lightweight applications in mobile devices, and it significantly deteriorates when a large transaction is issued such as high resolution medical images or massive health dataset, which is unlikely to occur in embedded systems but is quite common in other systems. Such transactions do not fit in the in-memory buffer of SQLite, and SQLite enforces memory reclamation as they are processed. The problem is that the current SQLite buffer management scheme does not effectively manage these cases, and the naïve reclamation scheme used significantly increases the user-perceived latency. Motivated by this limitation, this paper identifies the causes of high latency during processing of a large transaction, and overcomes the limitation via proactive and coarse-grained memory cleaning in SQLite.The proposed memory reclamation scheme was implemented in SQLite 3.29, and measurement studies with a prototype implementation demonstrated that the SQLite operation latency decreases by 13% on an average and up to 17.3% with our memory reclamation scheme as compared to that of the original version. Full article
(This article belongs to the Special Issue Disease Prediction, Machine Learning, and Healthcare)
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Article
Predicting Methane Concentration in Longwall Regions Using Artificial Neural Networks
Int. J. Environ. Res. Public Health 2019, 16(8), 1406; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph16081406 - 18 Apr 2019
Cited by 41 | Viewed by 1579
Abstract
Methane, which is released during mining exploitation, represents a serious threat to this process. This is because the gas may ignite or cause an explosion. Both of these phenomena are extremely dangerous. High levels of methane concentration in mine headings disrupt mining operations [...] Read more.
Methane, which is released during mining exploitation, represents a serious threat to this process. This is because the gas may ignite or cause an explosion. Both of these phenomena are extremely dangerous. High levels of methane concentration in mine headings disrupt mining operations and cause the risk of fire or explosion. Therefore, it is necessary to monitor and predict its concentration in the areas of ongoing mining exploitation. The paper presents the results of tests performed to improve work safety. The article presents the methodology of using artificial neural networks for predicting methane concentration values in one mining area. The objective of the paper is to develop an effective method for forecasting methane concentration in the mining industry. The application of neural networks for this purpose represents one of the first attempts in this respect. The method developed makes use of direct methane concentration values measured by a system of sensors located in the exploitation area. The forecasting model was built on the basis of a Multilayer Perceptron (MLP) network. The corresponding calculations were performed using a three-layered network with non-linear activation functions. The results obtained in the form of methane concentration prediction demonstrated minor errors in relation to the recorded values of this concentration. This offers an opportunity for a broader application of intelligent systems for effective prediction of mining hazards. Full article
(This article belongs to the Special Issue Disease Prediction, Machine Learning, and Healthcare)
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