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Applied Sciences
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The Application of Data Mining to Health Data
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The Application of Data Mining to Health Data

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

Deadline for manuscript submissions: closed (31 August 2020) | Viewed by 40870

Special Issue Editor

Dr. José Machado

E-Mail Website1 Website2
Guest Editor
Computer Science and Technology, ALGORITMI Research Centre, University of Minho, 4710-057 Braga, Portugal
Interests: knowledge engineering; health informatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We invite authors to submit unpublished work to the Special Issue “The Application of Data Mining to Health Data” in Applied Sciences. Authors are invited to contribute with research papers, case studies, and demonstrations that present original scientific results, methodological aspects, concepts, and approaches in the multidisciplinary field of data mining applied to healthcare, as well as related issues and challenges.

The Special Issue “The Application of Data Mining to Health Data” intends to be a manuscript where researchers, practitioners, and industry representatives can present and discuss ongoing work and latest research results of meaningful contributions into enabling technologies and emerging topics regarding reliable innovative solutions applied to healthcare for enhancing human quality of life, as well as related issues and challenges. It also intends to cover several dimensions of original research as regards theoretical, methodological, and technological developments, and new applications. Therefore, the main goal of this Special Issue is to contribute to the development of new approaches in data mining and reliable enabling technologies that will enhance human quality of life, leading to healthier, innovative, and secure societies.

Topics to be discussed in this Special Issue include (but are not limited to) the following:

  • Data mining;
  • Machine learning;
  • Artificial intelligence systems;
  • CDSS – clinical decision support systems;
  • Pervasive healthcare;
  • Knowledge discovery and embedded analytics;
  • Knowledge representation and reasoning;
  • Statistical analysis and characterization of health data;
  • Natural language processing—mining free text in electronic medical records;
  • Meaningful use of health data for improved patient care, treatment outcomes, and cost-reduction;
  • Pattern detection and hypothesis generation data.

Prof. José Manuel Ferreira Machado
Guest Editor

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 submissions that pass pre-check are 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 2400 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

  • data mining
  • machine learning
  • artificial intelligence
  • clinical decision support systems
  • knowledge extraction
  • healthcare

Published Papers (5 papers)

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Research

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17 pages, 1289 KiB  
Article
Recommendation System Using Autoencoders
by Diana Ferreira, Sofia Silva, António Abelha and José Machado
Appl. Sci. 2020, 10(16), 5510; https://doi.org/10.3390/app10165510 - 10 Aug 2020
Cited by 38 | Viewed by 14549
Abstract
The magnitude of the daily explosion of high volumes of data has led to the emergence of the Big Data paradigm. The ever-increasing amount of information available on the Internet makes it increasingly difficult for individuals to find what they need quickly and [...] Read more.
The magnitude of the daily explosion of high volumes of data has led to the emergence of the Big Data paradigm. The ever-increasing amount of information available on the Internet makes it increasingly difficult for individuals to find what they need quickly and easily. Recommendation systems have appeared as a solution to overcome this problem. Collaborative filtering is widely used in this type of systems, but high dimensions and data sparsity are always a main problem. With the idea of deep learning gaining more importance, several works have emerged to improve this type of filtering. In this article, a product recommendation system is proposed where an autoencoder based on a collaborative filtering method is employed. A comparison of this model with the Singular Value Decomposition is made and presented in the results section. Our experiment shows a very low Root Mean Squared Error (RMSE) value, considering that the recommendations presented to the users are in line with their interests and are not affected by the data sparsity problem as the datasets are very sparse, 0.996. The results are quite promising achieving an RMSE value of 0.029 in the first dataset and 0.010 in the second one. Full article
(This article belongs to the Special Issue The Application of Data Mining to Health Data)
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13 pages, 1111 KiB  
Article
A Combined Visualization Method for Multivariate Data Analysis. Application to Knee Kinematic and Clinical Parameters Relationships
by Fatima Bensalma, Glen Richardson, Youssef Ouakrim, Alexandre Fuentes, Michael Dunbar, Nicola Hagemeister and Neila Mezghani
Appl. Sci. 2020, 10(5), 1762; https://doi.org/10.3390/app10051762 - 04 Mar 2020
Cited by 2 | Viewed by 3115
Abstract
This paper aims to analyze the correlation structure between the kinematic and clinical parameters of an end-staged knee osteoarthritis population. The kinematic data are a set of characteristics derived from 3D knee kinematic patterns. The clinical parameters include the answers of a clinical [...] Read more.
This paper aims to analyze the correlation structure between the kinematic and clinical parameters of an end-staged knee osteoarthritis population. The kinematic data are a set of characteristics derived from 3D knee kinematic patterns. The clinical parameters include the answers of a clinical questionnaire and the patient’s demographic characteristics. The proposed method performs, first, a regularized canonical correlation analysis (RCCA) to evaluate the multivariate relationship between the clinical and kinematic datasets, and second, a combined visualization method to better understand the relationships between these multivariate data. Results show the efficiency of using different and complementary visual representation tools to highlight hidden relationships and find insights in data. Full article
(This article belongs to the Special Issue The Application of Data Mining to Health Data)
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22 pages, 1506 KiB  
Article
Incremental Algorithm for Association Rule Mining under Dynamic Threshold
by Iyad Aqra, Norjihan Abdul Ghani, Carsten Maple, José Machado and Nader Sohrabi Safa
Appl. Sci. 2019, 9(24), 5398; https://doi.org/10.3390/app9245398 - 10 Dec 2019
Cited by 24 | Viewed by 5528
Abstract
Data mining is essentially applied to discover new knowledge from a database through an iterative process. The mining process may be time consuming for massive datasets. A widely used method related to knowledge discovery domain refers to association rule mining (ARM) approach, despite [...] Read more.
Data mining is essentially applied to discover new knowledge from a database through an iterative process. The mining process may be time consuming for massive datasets. A widely used method related to knowledge discovery domain refers to association rule mining (ARM) approach, despite its shortcomings in mining large databases. As such, several approaches have been prescribed to unravel knowledge. Most of the proposed algorithms addressed data incremental issues, especially when a hefty amount of data are added to the database after the latest mining process. Three basic manipulation operations performed in a database include add, delete, and update. Any method devised in light of data incremental issues is bound to embed these three operations. The changing threshold is a long-standing problem within the data mining field. Since decision making refers to an active process, the threshold is indeed changeable. Accordingly, the present study proposes an algorithm that resolves the issue of rescanning a database that had been mined previously and allows retrieval of knowledge that satisfies several thresholds without the need to learn the process from scratch. The proposed approach displayed high accuracy in experimentation, as well as reduction in processing time by almost two-thirds of the original mining execution time. Full article
(This article belongs to the Special Issue The Application of Data Mining to Health Data)
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15 pages, 3714 KiB  
Article
Glucose Data Classification for Diabetic Patient Monitoring
by Amine Rghioui, Jaime Lloret, Lorena Parra, Sandra Sendra and Abdelmajid Oumnad
Appl. Sci. 2019, 9(20), 4459; https://doi.org/10.3390/app9204459 - 21 Oct 2019
Cited by 22 | Viewed by 6795
Abstract
Living longer and healthier is the wish of all patients. Therefore, to design effective solutions for this objective, the concept of Big Data in the health field can be integrated. Our work proposes a patient monitoring system based on Internet of Things (IoT) [...] Read more.
Living longer and healthier is the wish of all patients. Therefore, to design effective solutions for this objective, the concept of Big Data in the health field can be integrated. Our work proposes a patient monitoring system based on Internet of Things (IoT) and a diagnostic prediction tool for diabetic patients. This system provides real-time blood glucose readings and information on blood glucose levels. It monitors blood glucose levels at regular intervals. The proposed system aims to prevent high blood sugar and significant glucose fluctuations. The system provides a precise result. The collected and stored data will be classified by using several classification algorithms to predict glucose levels in diabetic patients. The main advantage of this system is that the blood glucose level is reported instantly; it can be lowered or increased. Full article
(This article belongs to the Special Issue The Application of Data Mining to Health Data)
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Review

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27 pages, 602 KiB  
Review
Machine Learning Applied to Diagnosis of Human Diseases: A Systematic Review
by Nuria Caballé-Cervigón, José L. Castillo-Sequera, Juan A. Gómez-Pulido, José M. Gómez-Pulido and María L. Polo-Luque
Appl. Sci. 2020, 10(15), 5135; https://doi.org/10.3390/app10155135 - 26 Jul 2020
Cited by 53 | Viewed by 9873
Abstract
Human healthcare is one of the most important topics for society. It tries to find the correct effective and robust disease detection as soon as possible to patients receipt the appropriate cares. Because this detection is often a difficult task, it becomes necessary [...] Read more.
Human healthcare is one of the most important topics for society. It tries to find the correct effective and robust disease detection as soon as possible to patients receipt the appropriate cares. Because this detection is often a difficult task, it becomes necessary medicine field searches support from other fields such as statistics and computer science. These disciplines are facing the challenge of exploring new techniques, going beyond the traditional ones. The large number of techniques that are emerging makes it necessary to provide a comprehensive overview that avoids very particular aspects. To this end, we propose a systematic review dealing with the Machine Learning applied to the diagnosis of human diseases. This review focuses on modern techniques related to the development of Machine Learning applied to diagnosis of human diseases in the medical field, in order to discover interesting patterns, making non-trivial predictions and useful in decision-making. In this way, this work can help researchers to discover and, if necessary, determine the applicability of the machine learning techniques in their particular specialties. We provide some examples of the algorithms used in medicine, analysing some trends that are focused on the goal searched, the algorithm used, and the area of applications. We detail the advantages and disadvantages of each technique to help choose the most appropriate in each real-life situation, as several authors have reported. The authors searched Scopus, Journal Citation Reports (JCR), Google Scholar, and MedLine databases from the last decades (from 1980s approximately) up to the present, with English language restrictions, for studies according to the objectives mentioned above. Based on a protocol for data extraction defined and evaluated by all authors using PRISMA methodology, 141 papers were included in this advanced review. Full article
(This article belongs to the Special Issue The Application of Data Mining to Health Data)
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