Special Issue "Data Analytics in Smart Healthcare"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (15 February 2019).

Special Issue Editors

Prof. Miltiadis D. Lytras
E-Mail Website
Guest Editor
1. School of Business, Deree—The American College of Greece, 6 Gravias Street GR-153 42 Aghia Paraskevi, Athens, Greece
2. Effat University, Jeddah, Saudi Arabia
Interests: cognitive computing; artificial intelligence; data science; bioinformatics; innovation; big data research; data mining; emerging technologies; information systems; technology driven innovation; knowledge management; semantic web
Special Issues and Collections in MDPI journals
Dr. Kwok Tai Chui
E-Mail Website
Guest Editor
Department of Technology, School of Science and Technology, The Open University of Hong Kong, Hong Kong SAR
Interests: big data; bioinformatics; computational intelligence; data science; energy monitoring and management; intelligent transportation; optimization; semantic web
Special Issues and Collections in MDPI journals
Prof. Anna Visvizi
E-Mail Website
Guest Editor
1. School of Business, Deree—The American College of Greece, 6 Gravias Street GR-153 42 Aghia Paraskevi Athens, Greece 2. Effat University, Jeddah, Saudi Arabia
Interests: smart cities; migration; innovation networks; international business; political economy; economic integration; politics; EU, Central Europe, China
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Every day 2.5 quintillion bytes data is generated which is an unimaginable figure to human beings and even machine. In recent decades a lot of data analytics techniques (especially artificial intelligence) have been presented to investigate the efficacy of data driven business intelligence application all over the world. With the tremendous growth of primary data volumes and diversity in every domain, they play an ever more crucial role enabling researchers and enterprises to formulate processing and analysis methods to extract latent information from multiple data resources and to leverage a broad range of data handling and computational platforms.

Health is wealth. The world is looking for more and more data analytics in healthcare to relieve medical problems in medical staff shortage, ageing population, people living alone and quality of life. There have been various research challenges and opportunities that lead to tremendous research publication in healthcare in recent decades.

This special issue aims to consolidate recent advances in data analytics for healthcare, research in theory and applications. Pilot studies in healthcare are especially welcome. Topics of interest for the special issue include (but are not limited to)

  • Descriptive analytics
  • Diagnostic analytics (e.g. ischaemic heart diseases, stroke, diabetes mellitus)
  • Predictive analytics
  • Prescriptive analytics
  • Text analytics
  • Artificial intelligence applications in healthcare
  • Data mining
  • Big data analysis
  • Optimization algorithms in healthcare
  • Missing biomedical data handling
  • Biomedical image/signal (pre)processing techniques for healthcare applications
  • Sensor data quality and reliability
  • Feature extraction in healthcare applications
  • Cloud computing
  • Complex network analysis
  • Data security
Prof. Miltiadis D. Lytras
Dr. Kwok Tai Chui
Prof. Anna Visvizi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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. Applied Sciences 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 1500 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.

Published Papers (14 papers)

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Editorial

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Open AccessEditorial
Data Analytics in Smart Healthcare: The Recent Developments and Beyond
Appl. Sci. 2019, 9(14), 2812; https://doi.org/10.3390/app9142812 - 14 Jul 2019
Abstract
The concepts of the smart city and the Internet of Things (IoT) have been facilitating the rollout of medical devices and systems to capture valuable information of humanity. A lot of artificial intelligence techniques have been demonstrated to be effective in smart city [...] Read more.
The concepts of the smart city and the Internet of Things (IoT) have been facilitating the rollout of medical devices and systems to capture valuable information of humanity. A lot of artificial intelligence techniques have been demonstrated to be effective in smart city applications like energy, transportation, retail and control. In recent decade, retardation of the adoption of data analytics algorithms and systems in healthcare has been decreasing, and there is tremendous growth in data analytics research on healthcare data. The results of analytics aim at improving people’s quality of life as well as relieving the issue of medical shortages. In this special issue “Data Analytics in Smart Healthcare”, thirteen (13) papers have been published as the representative examples of recent developments. Guest Editors also highlight some emergent topics and opening challenges in healthcare analytics which follow the visions of the movement of healthcare analytics research. Full article
(This article belongs to the Special Issue Data Analytics in Smart Healthcare)

Research

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Open AccessFeature PaperArticle
A Novel MOGA-SVM Multinomial Classification for Organ Inflammation Detection
Appl. Sci. 2019, 9(11), 2284; https://doi.org/10.3390/app9112284 - 03 Jun 2019
Cited by 6
Abstract
Wrist pulse signal (WPS) contains crucial information of humans’ health condition. It can serve as an alternative method for diagnosing of organ inflammation instead of traditional clinical measurement. In this paper, a novel multi-objective genetic algorithm based support vector machine (MOGA-SVM) has been [...] Read more.
Wrist pulse signal (WPS) contains crucial information of humans’ health condition. It can serve as an alternative method for diagnosing of organ inflammation instead of traditional clinical measurement. In this paper, a novel multi-objective genetic algorithm based support vector machine (MOGA-SVM) has been proposed for the multinomial classification of the inflammations of appendix, pancreas, and duodenum. A customized similarity kernel (KCS) has been optimally designed. The performance of multinomial classification using KCS is compared with five types of kernels, linear, radial basis function (RBF), polynomial and sigmoid kernel, as well as mixtures of polynomial and RBF, to verify the effectiveness of KCS. The sensitivity, specificity and accuracy (Acc) of the proposed method are 92%, 91.2%, and 91.6% respectively. The results have demonstrated that KCS improves the accuracy of classification from 8.9% to 59.6%. When compared to related work, the proposed method increases the performance by more than 10%. It is believed that WPS can serve as alternative measures to diagnose organ inflammations. Full article
(This article belongs to the Special Issue Data Analytics in Smart Healthcare)
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Open AccessArticle
Data Analytics and Its Advantages for Addressing the Complexity of Healthcare: A Simulated Zika Case Study Example
Appl. Sci. 2019, 9(11), 2208; https://doi.org/10.3390/app9112208 - 29 May 2019
Cited by 1
Abstract
The need to control rising costs in healthcare has led to an increase in the use of data analytics to develop more efficient healthcare business models. This article discusses a simulation that uses data analytics to minimize the number of physicians and nurses [...] Read more.
The need to control rising costs in healthcare has led to an increase in the use of data analytics to develop more efficient healthcare business models. This article discusses a simulation that uses data analytics to minimize the number of physicians and nurses needed in healthcare facilities during a crisis situation. Using a hypothetical emergency scenario, the hospital uses a healthcare analytical system to predict the necessary resources to govern the situation. Based on historical data regarding the flow of patients through the facility, a discrete-event simulation estimates resource scheduling and the resulting impact on both wait times and personnel demand. Furthermore, the value of multiple replications for discrete-event simulation models is discussed and defined, along with factors that enable greater control of multiple design points with this simulated experiment. The results of this study demonstrate the value of simulation modeling in effective resource planning. The addition of only a single doctor significantly reduced predicted wait times for patients during the crisis. Further, the findings support the use of data analytics and predictive modeling to mitigate rising healthcare costs in the United States through efficient planning and resource allocation. Full article
(This article belongs to the Special Issue Data Analytics in Smart Healthcare)
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Open AccessArticle
Skin Aging Estimation Scheme Based on Lifestyle and Dermoscopy Image Analysis
Appl. Sci. 2019, 9(6), 1228; https://doi.org/10.3390/app9061228 - 23 Mar 2019
Cited by 1
Abstract
Besides genetic characteristics, people also undergo a process of skin aging under the influence of diverse factors such as sun exposure, food intake, sleeping patterns, and drinking habits, which are closely related to their personal lifestyle. So far, many studies have been conducted [...] Read more.
Besides genetic characteristics, people also undergo a process of skin aging under the influence of diverse factors such as sun exposure, food intake, sleeping patterns, and drinking habits, which are closely related to their personal lifestyle. So far, many studies have been conducted to analyze skin conditions quantitatively. However, to describe the current skin condition or predict future skin aging effectively, we need to understand the correlation between skin aging and lifestyle. In this study, we first demonstrate how to trace people’s skin condition accurately using scale-invariant feature transform and the color histogram intersection method. Then, we show how to estimate skin texture aging depending on the lifestyle by considering various features from face, neck, and hand dermoscopy images. Lastly, we describe how to predict future skin conditions in terms of skin texture features. Based on the Pearson correlation, we describe the correlation between skin aging and lifestyle, and estimate skin aging according to lifestyle using the polynomial regression and support vector regression models. We evaluate the performance of our proposed scheme through various experiments. Full article
(This article belongs to the Special Issue Data Analytics in Smart Healthcare)
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Open AccessArticle
Probability Analysis of Hypertension-Related Symptoms Based on XGBoost and Clustering Algorithm
Appl. Sci. 2019, 9(6), 1215; https://doi.org/10.3390/app9061215 - 22 Mar 2019
Cited by 1
Abstract
In this paper, cluster analysis and the XGBoost method are used to analyze the related symptoms of various types of young hypertensive patients, and finally guide patients to target treatment. Hypertension is a chronic disease that is common worldwide. The incidence of it [...] Read more.
In this paper, cluster analysis and the XGBoost method are used to analyze the related symptoms of various types of young hypertensive patients, and finally guide patients to target treatment. Hypertension is a chronic disease that is common worldwide. The incidence of it is increasing, and the age level of patients is decreasing year by year. Effective treatment of youth hypertension has become a problem in the world. In this paper, young hypertension patients are classified into two groups by cluster analysis; the proportion of different hypertension related symptoms in each group of patients is then counted; and after verifying the prediction accuracy of the XGBoost model with 10-fold cross-validation, the accuracy of clustering is calculated by the XGBoost method. The final result shows that there are significant differences in symptomatic entropy between patients with type II hypertension and those with type I hypertension. Patients with type II hypertension are more likely to have symptoms of ventricular hypertrophy and microalbuminuria. Through this analysis, patients can have preventive treatment according to their own situation, and this can reduce the burden of medical expenses and prevent major diseases. Applying the data analysis into the medical field has great practical significance. Full article
(This article belongs to the Special Issue Data Analytics in Smart Healthcare)
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Open AccessArticle
Classification of Pulmonary CT Images by Using Hybrid 3D-Deep Convolutional Neural Network Architecture
Appl. Sci. 2019, 9(5), 940; https://doi.org/10.3390/app9050940 - 06 Mar 2019
Cited by 3
Abstract
Lung cancer is the most common cause of cancer-related deaths worldwide. Hence, the survival rate of patients can be increased by early diagnosis. Recently, machine learning methods on Computed Tomography (CT) images have been used in the diagnosis of lung cancer to accelerate [...] Read more.
Lung cancer is the most common cause of cancer-related deaths worldwide. Hence, the survival rate of patients can be increased by early diagnosis. Recently, machine learning methods on Computed Tomography (CT) images have been used in the diagnosis of lung cancer to accelerate the diagnosis process and assist physicians. However, in conventional machine learning techniques, using handcrafted feature extraction methods on CT images are complicated processes. Hence, deep learning as an effective area of machine learning methods by using automatic feature extraction methods could minimize the process of feature extraction. In this study, two Convolutional Neural Network (CNN)-based models were proposed as deep learning methods to diagnose lung cancer on lung CT images. To investigate the performance of the two proposed models (Straight 3D-CNN with conventional softmax and hybrid 3D-CNN with Radial Basis Function (RBF)-based SVM), the altered models of two-well known CNN architectures (3D-AlexNet and 3D-GoogleNet) were considered. Experimental results showed that the performance of the two proposed models surpassed 3D-AlexNet and 3D-GoogleNet. Furthermore, the proposed hybrid 3D-CNN with SVM achieved more satisfying results (91.81%, 88.53% and 91.91% for accuracy rate, sensitivity and precision respectively) compared to straight 3D-CNN with softmax in the diagnosis of lung cancer. Full article
(This article belongs to the Special Issue Data Analytics in Smart Healthcare)
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Open AccessArticle
Statistical Edge Detection and Circular Hough Transform for Optic Disk Localization
Appl. Sci. 2019, 9(2), 350; https://doi.org/10.3390/app9020350 - 21 Jan 2019
Cited by 4
Abstract
Accurate and efficient localization of the optic disk (OD) in retinal images is an essential process for the diagnosis of retinal diseases, such as diabetic retinopathy, papilledema, and glaucoma, in automatic retinal analysis systems. This paper presents an effective and robust framework for [...] Read more.
Accurate and efficient localization of the optic disk (OD) in retinal images is an essential process for the diagnosis of retinal diseases, such as diabetic retinopathy, papilledema, and glaucoma, in automatic retinal analysis systems. This paper presents an effective and robust framework for automatic detection of the OD. The framework begins with the process of elimination of the pixels below the average brightness level of the retinal images. Next, a method based on the modified robust rank order was used for edge detection. Finally, the circular Hough transform (CHT) was performed on the obtained retinal images for OD localization. Three public datasets were used to evaluate the performance of the proposed method. The optic disks were successfully located with the success rates of 100%, 96.92%, and 98.88% for the DRIVE, DIARETDB0, and DIARETDB1 datasets, respectively. Full article
(This article belongs to the Special Issue Data Analytics in Smart Healthcare)
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Open AccessArticle
Relationship between Continuity of Care in the Multidisciplinary Treatment of Patients with Diabetes and Their Clinical Results
Appl. Sci. 2019, 9(2), 268; https://doi.org/10.3390/app9020268 - 14 Jan 2019
Cited by 1
Abstract
Multidisciplinary treatment and continuity of care throughout treatment are important for ensuring metabolic control and avoiding complications in diabetic patients. This study examines the relationship between continuity of care of the treating disciplines and clinical evolution of patients. Data from 1836 adult patients [...] Read more.
Multidisciplinary treatment and continuity of care throughout treatment are important for ensuring metabolic control and avoiding complications in diabetic patients. This study examines the relationship between continuity of care of the treating disciplines and clinical evolution of patients. Data from 1836 adult patients experiencing type 2 diabetes mellitus were analyzed, in a period between 12 and 24 months. Continuity was measured by using four well known indices: Usual Provider Continuity (UPC), Continuity of Care Index (COCI), Herfindahl Index (HI), and Sequential Continuity (SECON). Patients were divided into five segments according to metabolic control: well-controlled, worsened, moderately decompensated, highly decompensated, and improved. Well-controlled patients had higher continuity by physicians according to UPC and HI indices (p-values 0.029 and <0.003), whereas highly decompensated patients had less continuity in HI (p-value 0.020). Continuity for nurses was similar, with a greater continuity among well-controlled patients (p-values 0.015 and 0.001 for UPC and HI indices), and less among highly decompensated patients (p-values 0.004 and <0.001 for UPC and HI indices). Improved patients had greater adherence to the protocol than those who worsened. The SECON index showed no significant differences across the disciplines. This study identified a relationship between physicians and nurse’s continuity of care and metabolic control in patients with diabetes, consistent with qualitative findings that highlight the role of nurses in treatment. Full article
(This article belongs to the Special Issue Data Analytics in Smart Healthcare)
Open AccessArticle
A Systematic Review of Open Source Clinical Software on GitHub for Improving Software Reuse in Smart Healthcare
Appl. Sci. 2019, 9(1), 150; https://doi.org/10.3390/app9010150 - 03 Jan 2019
Cited by 1
Abstract
The plethora of open source clinical software offers great reuse opportunities for developers to build clinical tools at lower cost and at a faster pace. However, the lack of research on open source clinical software poses a challenge for software reuse in clinical [...] Read more.
The plethora of open source clinical software offers great reuse opportunities for developers to build clinical tools at lower cost and at a faster pace. However, the lack of research on open source clinical software poses a challenge for software reuse in clinical software development. This paper aims to help clinical developers better understand open source clinical software by conducting a thorough investigation of open source clinical software hosted on GitHub. We first developed a data pipeline that automatically collected and preprocessed GitHub data. Then, a deep analysis with several methods, such as statistical analysis, hypothesis testing, and topic modeling, was conducted to reveal the overall status and various characteristics of open source clinical software. There were 14,971 clinical-related GitHub repositories created during the last 10 years, with an average annual growth rate of 55%. Among them, 12,919 are open source clinical software. Our analysis unveiled a number of interesting findings: Popular open source clinical software in terms of the number of stars, most productive countries that contribute to the community, important factors that make an open source clinical software popular, and 10 main groups of open source clinical software. The results can assist both researchers and practitioners, especially newcomers, in understanding open source clinical software. Full article
(This article belongs to the Special Issue Data Analytics in Smart Healthcare)
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Open AccessArticle
Estimation of Association between Healthcare System Efficiency and Policy Factors for Public Health
Appl. Sci. 2018, 8(12), 2674; https://doi.org/10.3390/app8122674 - 19 Dec 2018
Cited by 3
Abstract
Objective: To assess the association between the healthcare system’s efficiency and policy factors (the types of healthcare systems and various health policy indicators). Methods: In this study, a data envelopment analysis (DEA) with bootstrapping was applied to the healthcare system’s efficiency to correct [...] Read more.
Objective: To assess the association between the healthcare system’s efficiency and policy factors (the types of healthcare systems and various health policy indicators). Methods: In this study, a data envelopment analysis (DEA) with bootstrapping was applied to the healthcare system’s efficiency to correct the bias of efficiency scores and to rank countries appropriately. We analyzed data mainly from the OECD (Organization for Economic Co-operation and Development) Health Data from 2014. After obtaining the efficiency score result, we analyzed which policy factor caused the inefficiency of the healthcare system by Tobit Regression. Results: Based on five types of healthcare system classification, the result suggested that the social health insurance (e.g., Austria, Germany, Switzerland) showed the lowest efficiency score on average when compared to other types of systems, but evidence of a statistically significant difference in healthcare efficiency among four types of healthcare systems was not found. It was shown that the pure technological efficiency of the healthcare system was negatively influenced by two main factors: user choice for basic insurance coverage and degree of decentralization to sub-national governments. Conclusions: Our findings suggest that countries with relatively low healthcare system efficiency may learn from countries that implement policies related to a low level of user choice and a high level of centralization to achieve more economical allocation of their healthcare resources. Full article
(This article belongs to the Special Issue Data Analytics in Smart Healthcare)
Open AccessArticle
Data Analysis and Forecasting of Tuberculosis Prevalence Rates for Smart Healthcare Based on a Novel Combination Model
Appl. Sci. 2018, 8(9), 1693; https://doi.org/10.3390/app8091693 - 18 Sep 2018
Cited by 3
Abstract
In recent years, healthcare has attracted much attention, which is looking for more and more data analytics in healthcare to relieve medical problems in medical staff shortage, ageing population, people living alone, and quality of life. Data mining, analysis, and forecasting play a [...] Read more.
In recent years, healthcare has attracted much attention, which is looking for more and more data analytics in healthcare to relieve medical problems in medical staff shortage, ageing population, people living alone, and quality of life. Data mining, analysis, and forecasting play a vital role in modern social and medical fields. However, how to select a proper model to mine and analyze the relevant medical information in the data is not only an extremely challenging problem, but also a concerning problem. Tuberculosis remains a major global health problem despite recent and continued progress in prevention and treatment. There is no doubt that the effective analysis and accurate forecasting of global tuberculosis prevalence rates lay a solid foundation for the construction of an epidemic disease warning and monitoring system from a global perspective. In this paper, the tuberculosis prevalence rate time series for four World Bank income groups are targeted. Kruskal–Wallis analysis of variance and multiple comparison tests are conducted to determine whether the differences of tuberculosis prevalence rates for different income groups are statistically significant or not, and a novel combined forecasting model with its weights optimized by a recently developed artificial intelligence algorithm—cuckoo search—is proposed to forecast the hierarchical tuberculosis prevalence rates from 2013 to 2016. Numerical results show that the developed combination model is not only simple, but is also able to satisfactorily approximate the actual tuberculosis prevalence rate, and can be an effective tool in mining and analyzing big data in the medical field. Full article
(This article belongs to the Special Issue Data Analytics in Smart Healthcare)
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Open AccessArticle
The Prostate Clinical Outlook (PCO) Classifier Application for Predicting Biochemical Recurrences in Patients Treated by Stereotactic Body Radiation Therapy (SBRT)
Appl. Sci. 2018, 8(9), 1620; https://doi.org/10.3390/app8091620 - 12 Sep 2018
Cited by 1
Abstract
(1) Background: Prostate cancer risk classifiers have been used for predicting surgical and radiation therapy outcomes; however, a classifier for predicting biochemical recurrence (BCR) in patients undergoing stereotactic body radiation therapy (SBRT) is not available. We attempted to develop a model that creates [...] Read more.
(1) Background: Prostate cancer risk classifiers have been used for predicting surgical and radiation therapy outcomes; however, a classifier for predicting biochemical recurrence (BCR) in patients undergoing stereotactic body radiation therapy (SBRT) is not available. We attempted to develop a model that creates a risk classifier to predict BCR in patients considering SBRT. (2) Methods: We studied the outcomes of 809 patients treated with SBRT between August 2007 and November 2016. We used Cox regression analysis with time to BCR as the outcome to develop a model that calculates a prostate clinical outlook (PCO) score based on age at diagnosis, clinical-radiological staging, and a modified risk level. We then created the PCO classifier application, which uses the model we created to categorize patients into risk groups based on multiple factors. We assessed the concordance index (c-index) to determine the accuracy of the PCO classifier application and compared the results to the D’Amico and Kattan nomogram classifications. (3) Results: The calculated PCO scores ranged from 0 to 156 points. The PCO classifier application categorized patients into three risk-groups, with 5-year BCR-free survival rates of 98.3% for low risk (n = 137), 95.4% for intermediate risk (n = 570), and 86.4% for high risk (n = 102). We demonstrated the improved prognostic power of the PCO classifier application, with a c-index of 0.75 (training set) and 0.67 (validation set); the c-index of the Kattan nomogram was 0.62 and 0.63, respectively, and that of the D’Amico classifier was 0.64 and 0.64, respectively. (4) Conclusions: The PCO classifier application is a predictive tool for employing readily available clinical parameters to stratify prostate cancer patients and to predict the probability of BCR after SBRT. Full article
(This article belongs to the Special Issue Data Analytics in Smart Healthcare)
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Open AccessArticle
Hybrid Prediction Model for Type 2 Diabetes and Hypertension Using DBSCAN-Based Outlier Detection, Synthetic Minority Over Sampling Technique (SMOTE), and Random Forest
Appl. Sci. 2018, 8(8), 1325; https://doi.org/10.3390/app8081325 - 08 Aug 2018
Cited by 10
Abstract
As the risk of diseases diabetes and hypertension increases, machine learning algorithms are being utilized to improve early stage diagnosis. This study proposes a Hybrid Prediction Model (HPM), which can provide early prediction of type 2 diabetes (T2D) and hypertension based on input [...] Read more.
As the risk of diseases diabetes and hypertension increases, machine learning algorithms are being utilized to improve early stage diagnosis. This study proposes a Hybrid Prediction Model (HPM), which can provide early prediction of type 2 diabetes (T2D) and hypertension based on input risk-factors from individuals. The proposed HPM consists of Density-based Spatial Clustering of Applications with Noise (DBSCAN)-based outlier detection to remove the outlier data, Synthetic Minority Over-Sampling Technique (SMOTE) to balance the distribution of class, and Random Forest (RF) to classify the diseases. Three benchmark datasets were utilized to predict the risk of diabetes and hypertension at the initial stage. The result showed that by integrating DBSCAN-based outlier detection, SMOTE, and RF, diabetes and hypertension could be successfully predicted. The proposed HPM provided the best performance result as compared to other models for predicting diabetes as well as hypertension. Furthermore, our study has demonstrated that the proposed HPM can be applied in real cases in the IoT-based Health-care Monitoring System, so that the input risk-factors from end-user android application can be stored and analyzed in a secure remote server. The prediction result from the proposed HPM can be accessed by users through an Android application; thus, it is expected to provide an effective way to find the risk of diabetes and hypertension at the initial stage. Full article
(This article belongs to the Special Issue Data Analytics in Smart Healthcare)
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Open AccessArticle
Comparing Deep Learning and Classical Machine Learning Approaches for Predicting Inpatient Violence Incidents from Clinical Text
Appl. Sci. 2018, 8(6), 981; https://doi.org/10.3390/app8060981 - 15 Jun 2018
Cited by 3
Abstract
Machine learning techniques are increasingly being applied to clinical text that is already captured in the Electronic Health Record for the sake of delivering quality care. Applications for example include predicting patient outcomes, assessing risks, or performing diagnosis. In the past, good results [...] Read more.
Machine learning techniques are increasingly being applied to clinical text that is already captured in the Electronic Health Record for the sake of delivering quality care. Applications for example include predicting patient outcomes, assessing risks, or performing diagnosis. In the past, good results have been obtained using classical techniques, such as bag-of-words features, in combination with statistical models. Recently however Deep Learning techniques, such as Word Embeddings and Recurrent Neural Networks, have shown to possibly have even greater potential. In this work, we apply several Deep Learning and classical machine learning techniques to the task of predicting violence incidents during psychiatric admission using clinical text that is already registered at the start of admission. For this purpose, we use a novel and previously unexplored dataset from the Psychiatry Department of the University Medical Center Utrecht in The Netherlands. Results show that predicting violence incidents with state-of-the-art performance is possible, and that using Deep Learning techniques provides a relatively small but consistent improvement in performance. We finally discuss the potential implication of our findings for the psychiatric practice. Full article
(This article belongs to the Special Issue Data Analytics in Smart Healthcare)
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