Special Issue "Classification, Diagnosis and Prognosis of Diseases Using Machine Learning Algorithms"

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: 31 August 2021.

Special Issue Editors

Dr. Cornelio Yáñez Márquez
E-Mail Website
Guest Editor
Centro de Investigación en Computación, Instituto Politécnico Nacional, 07738 Ciudad de México, CDMX, Mexico
Interests: deep learning; associative models; machine learning; pattern recognition; neural networks; metaheuristics
Dr. Yenny Villuendas-Rey
E-Mail Website
Guest Editor
Centro de Innovación y Desarrollo Tecnológico en Cómputo, Instituto Politécnico Nacional, CDMX, México
Interests: optimization; bio-inspired algorithms; machine learning; rough sets; biomedical applications
Prof. Dr. Miltiadis D. Lytras
E-Mail Website
Guest Editor
Effat College of Engineering, Effat University, Jeddah P.O. Box 34689, 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

Special Issue Information

Dear Colleagues,

The arrival of the third millennium has brought impressive developments and advances in machine learning algorithms. Recent advances in deep learning, the algorithms of which are accelerated with CUDA hardware cards, deserve special mention. The applications of this type of algorithms have permeated a wide range of human activities, including the sensitive area of health research.

The contents of high impact research journals bear witness to the efforts of scientists on such relevant topics as the classification, diagnosis and prognosis of diseases. Given the speed with which these investigations are advancing, due to the rapid development of new hardware, software and application platforms, it is necessary to promote new investigations that support physicians and health researchers.

This Special Issue seeks unpublished contributions of high scientific quality on the topic of the classification, diagnosis and prognosis of diseases using machine learning algorithms.

Dr. Cornelio Yáñez Márquez
Dr. Yenny Villuendas-Rey
Prof. Dr. Miltiadis D. Lytras
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. Mathematics 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 1600 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

  • machine learning
  • classification of diseases
  • diagnosis of diseases
  • prognosis of diseases
  • cancer
  • chronic diseases
  • artificial intelligence
  • associative memories
  • deep learning
  • data mining
  • big data

Published Papers (2 papers)

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Research

Article
Classification of Diseases Using Machine Learning Algorithms: A Comparative Study
Mathematics 2021, 9(15), 1817; https://doi.org/10.3390/math9151817 - 31 Jul 2021
Viewed by 427
Abstract
Machine learning in the medical area has become a very important requirement. The healthcare professional needs useful tools to diagnose medical illnesses. Classifiers are important to provide tools that can be useful to the health professional for this purpose. However, questions arise: which [...] Read more.
Machine learning in the medical area has become a very important requirement. The healthcare professional needs useful tools to diagnose medical illnesses. Classifiers are important to provide tools that can be useful to the health professional for this purpose. However, questions arise: which classifier to use? What metrics are appropriate to measure the performance of the classifier? How to determine a good distribution of the data so that the classifier does not bias the medical patterns to be classified in a particular class? Then most important question: does a classifier perform well for a particular disease? This paper will present some answers to the questions mentioned above, making use of classification algorithms widely used in machine learning research with datasets relating to medical illnesses under the supervised learning scheme. In addition to state-of-the-art algorithms in pattern classification, we introduce a novelty: the use of meta-learning to determine, a priori, which classifier would be the ideal for a specific dataset. The results obtained show numerically and statistically that there are reliable classifiers to suggest medical diagnoses. In addition, we provide some insights about the expected performance of classifiers for such a task. Full article
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Article
Supervised Classification of Diseases Based on an Improved Associative Algorithm
Mathematics 2021, 9(13), 1458; https://doi.org/10.3390/math9131458 - 22 Jun 2021
Viewed by 727
Abstract
The linear associator is a classic associative memory model. However, due to its low performance, it is pertinent to note that very few linear associator applications have been published. The reason for this is that this model requires the vectors representing the patterns [...] Read more.
The linear associator is a classic associative memory model. However, due to its low performance, it is pertinent to note that very few linear associator applications have been published. The reason for this is that this model requires the vectors representing the patterns to be orthonormal, which is a big restriction. Some researchers have tried to create orthogonal projections to the vectors to feed the linear associator. However, this solution has serious drawbacks. This paper presents a proposal that effectively improves the performance of the linear associator when acting as a pattern classifier. For this, the proposal involves transforming the dataset using a powerful mathematical tool: the singular value decomposition. To perform the experiments, we selected fourteen medical datasets of two classes. All datasets exhibit balance, so it is possible to use accuracy as a performance measure. The effectiveness of our proposal was compared against nine supervised classifiers of the most important approaches (Bayes, nearest neighbors, decision trees, support vector machines, and neural networks), including three classifier ensembles. The Friedman and Holm tests show that our proposal had a significantly better performance than four of the nine classifiers. Furthermore, there are no significant differences against the other five, although three of them are ensembles. Full article
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