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Diagnostics
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Predictive Modelling in Healthcare
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Predictive Modelling in Healthcare

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 12845

Special Issue Editor

Dr. Eleni Georga

E-Mail Website
Guest Editor
1. Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, GR 45110 Ioannina, Greece
2. Institute of Molecular Biology and Biotechnology, Department of Biomedical Research—FORTH, University Campus of Ioannina, GR 45110 Ioannina, Greece
Interests: computational modelling of physiological systems; machine learning and pattern recognition-based analysis of biomedical data; AI-based healthcare recommendation systems

Special Issue Information

Dear Colleagues,

One individual’s biology, behaviour, and context can nowadays be precisely captured into new prospective data generated in the context of ad hoc cross-sectional or longitudinal clinical studies. Research questions defined upon a specific dataset and pertaining to the prediction, either diagnosis or prognosis, of health outcomes are formulated as machine learning (ML; inclusive of classical and deep learning) regression or classification multivariate functions of biomedical data. ML and linear system identification are essential for predicting time series data alike. Importantly, the advent of new guidelines contributes not only to enhancing the transparency of reporting of the conducted analyses but also to correcting the bias in the data-selection process and across the entire ML pipeline. In this context, the aim of this Special Issue is to present prediction model studies of health outcomes, tapping into systematic data quality assessment methods, novel ML architectures, robust cross-validation or external validation procedures and proper correctness and model relevance metrics. Special attention shall be placed on studies leveraging multimodal ML, multiscale modelling, adaptive learning of dynamic systems, and sparse ML. Whist high predictive performance is the mandate in predictive modelling in healthcare, additional quality properties, e.g., expressing interpretability, adversarial robustness, and fairness, are of paramount importance as well. In this direction, studies focusing on techniques for testing ML systems, as they are applied in predictive modelling paradigms in healthcare, shall be featured here.

Dr. Eleni Georga
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. Diagnostics 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 2600 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.

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

15 pages, 4458 KiB  
Article
Hybrid Majority Voting: Prediction and Classification Model for Obesity
by Dahlak Daniel Solomon, Shakir Khan, Sonia Garg, Gaurav Gupta, Abrar Almjally, Bayan Ibrahimm Alabduallah, Hatoon S. Alsagri, Mandour Mohamed Ibrahim and Alsadig Mohammed Adam Abdallah
Diagnostics 2023, 13(15), 2610; https://doi.org/10.3390/diagnostics13152610 - 7 Aug 2023
Cited by 35 | Viewed by 3865
Abstract
Because it is associated with most multifactorial inherited diseases like heart disease, hypertension, diabetes, and other serious medical conditions, obesity is a major global health concern. Obesity is caused by hereditary, physiological, and environmental factors, as well as poor nutrition and a lack [...] Read more.
Because it is associated with most multifactorial inherited diseases like heart disease, hypertension, diabetes, and other serious medical conditions, obesity is a major global health concern. Obesity is caused by hereditary, physiological, and environmental factors, as well as poor nutrition and a lack of exercise. Weight loss can be difficult for various reasons, and it is diagnosed via BMI, which is used to estimate body fat for most people. Muscular athletes, for example, may have a BMI in the obesity range even when they are not obese. Researchers from a variety of backgrounds and institutions devised different hypotheses and models for the prediction and classification of obesity using different approaches and various machine learning techniques. In this study, a majority voting-based hybrid modeling approach using a gradient boosting classifier, extreme gradient boosting, and a multilayer perceptron was developed. Seven distinct machine learning algorithms were used on open datasets from the UCI machine learning repository, and their respective accuracy levels were compared before the combined approaches were chosen. The proposed majority voting-based hybrid model for prediction and classification of obesity that was achieved has an accuracy of 97.16%, which is greater than both the individual models and the other hybrid models that have been developed. Full article
(This article belongs to the Special Issue Predictive Modelling in Healthcare)
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15 pages, 3364 KiB  
Article
DDPM: A Dengue Disease Prediction and Diagnosis Model Using Sentiment Analysis and Machine Learning Algorithms
by Gaurav Gupta, Shakir Khan, Vandana Guleria, Abrar Almjally, Bayan Ibrahimm Alabduallah, Tamanna Siddiqui, Bader M. Albahlal, Saad Abdullah Alajlan and Mashael AL-subaie
Diagnostics 2023, 13(6), 1093; https://doi.org/10.3390/diagnostics13061093 - 14 Mar 2023
Cited by 31 | Viewed by 8126
Abstract
The aedes mosquito-borne dengue viruses cause dengue fever, an arboviral disease (DENVs). In 2019, the World Health Organization forecasts a yearly occurrence of infections from 100 million to 400 million, the maximum number of dengue cases ever testified worldwide, prompting WHO to label [...] Read more.
The aedes mosquito-borne dengue viruses cause dengue fever, an arboviral disease (DENVs). In 2019, the World Health Organization forecasts a yearly occurrence of infections from 100 million to 400 million, the maximum number of dengue cases ever testified worldwide, prompting WHO to label the virus one of the world’s top ten public health risks. Dengue hemorrhagic fever can progress into dengue shock syndrome, which can be fatal. Dengue hemorrhagic fever can also advance into dengue shock syndrome. To provide accessible and timely supportive care and therapy, it is necessary to have indispensable practical instruments that accurately differentiate Dengue and its subcategories in the early stages of illness development. Dengue fever can be predicted in advance, saving one’s life by warning them to seek proper diagnosis and treatment. Predicting infectious diseases such as dengue is difficult, and most forecast systems are still in their primary stages. In developing dengue predictive models, data from microarrays and RNA-Seq have been used significantly. Bayesian inferences and support vector machine algorithms are two examples of statistical methods that can mine opinions and analyze sentiment from text. In general, these methods are not very strong semantically, and they only work effectively when the text passage inputs are at the level of the page or the paragraph; they are poor miners of sentiment at the level of the sentence or the phrase. In this research, we propose to construct a machine learning method to forecast dengue fever. Full article
(This article belongs to the Special Issue Predictive Modelling in Healthcare)
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