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Artificial Intelligence and Big Data for Healthcare and Biological Data...
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Artificial Intelligence and Big Data for Healthcare and Biological Data Processing

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Biological Processes and Systems".

Deadline for manuscript submissions: 20 May 2024 | Viewed by 4017

Special Issue Editor

Prof. Dr. Naomie Salim

E-Mail Website
Guest Editor
1. Faculty of Computing, Universiti Teknologi Malaysia, Johor Bahru 81310, Johor, Malaysia
2. UTM Big Data Centre, Ibnu Sina Institute for Scientific and Industrial Research, Universiti Teknologi Malaysia, Johor Bahru 81310, Johor, Malaysia
Interests: artificial intelligence; information retrieval; database systems; chemical informatics; big data analytics

Special Issue Information

Dear Colleagues,

The advent of artificial intelligence (AI) in healthcare has been a breakthrough, reshaping the way we diagnose, treat, manage, and monitor patients. The rise of data in healthcare and its complexity means that AI will increasingly be applied within the field. AI in healthcare is emerging in several ways, such as finding new links between genetic code, powering robots that assist in surgery, automating administrative tasks, personalizing treatment options, and much more.

AI in healthcare is expected to significantly redefine how we process healthcare data, diagnose diseases, develop treatments, and even prevent them altogether. From identifying new cancer treatments to improving patient experiences, AI in healthcare promises to be a game changer, leading the way toward a future where patients receive quality care and treatment faster and more accurately than ever.

This Special Issue aims to publish articles in the interdisciplinary area of healthcare and AI. This includes AI research on disease prevention, early diagnosis, diagnosis, treatment, etc. The topics include but are not limited to:

  • AI for personalized and predictive medicine;
  • Natural language processing in healthcare;
  • Computational genetics and genomics;
  • Data science and big data analytics for healthcare;
  • AI for public health and epidemiology;
  • Computation drug discovery and cheminformatics;
  • AI-based clinical decision making;
  • Medical knowledge engineering;
  • Machine learning and deep learning in healthcare;
  • AI for medical imaging;
  • The future of AI in healthcare.

Prof. Dr. Naomie Salim
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. Processes is an international peer-reviewed open access monthly 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

  • artificial intelligence
  • machine learning
  • healthcare
  • data science
  • personalized medicine
  • medicine
  • bioinformatics
  • drug discovery

Published Papers (3 papers)

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Research

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15 pages, 4784 KiB  
Article
Feature Selection of Microarray Data Using Simulated Kalman Filter with Mutation
by Nurhawani Ahmad Zamri, Nor Azlina Ab. Aziz, Thangavel Bhuvaneswari, Nor Hidayati Abdul Aziz and Anith Khairunnisa Ghazali
Processes 2023, 11(8), 2409; https://doi.org/10.3390/pr11082409 - 10 Aug 2023
Cited by 3 | Viewed by 969
Abstract
Microarrays have been proven to be beneficial for understanding the genetics of disease. They are used to assess many different types of cancers. Machine learning algorithms, like the artificial neural network (ANN), can be trained to determine whether a microarray sample is cancerous [...] Read more.
Microarrays have been proven to be beneficial for understanding the genetics of disease. They are used to assess many different types of cancers. Machine learning algorithms, like the artificial neural network (ANN), can be trained to determine whether a microarray sample is cancerous or not. The classification is performed using the features of DNA microarray data, which are composed of thousands of gene values. However, most of the gene values have been proven to be uninformative and redundant. Meanwhile, the number of the samples is significantly smaller in comparison to the number of genes. Therefore, this paper proposed the use of a simulated Kalman filter with mutation (SKF-MUT) for the feature selection of microarray data to enhance the classification accuracy of ANN. The algorithm is based on a metaheuristics optimization algorithm, inspired by the famous Kalman filter estimator. The mutation operator is proposed to enhance the performance of the original SKF in the selection of microarray features. Eight different benchmark datasets were used, which comprised: diffuse large b-cell lymphomas (DLBCL); prostate cancer; lung cancer; leukemia cancer; “small, round blue cell tumor” (SRBCT); brain tumor; nine types of human tumors; and 11 types of human tumors. These consist of both binary and multiclass datasets. The accuracy is taken as the performance measurement by considering the confusion matrix. Based on the results, SKF-MUT effectively selected the number of features needed, leading toward a higher classification accuracy ranging from 95% to 100%. Full article
(This article belongs to the Special Issue Artificial Intelligence and Big Data for Healthcare and Biological Data Processing)
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13 pages, 574 KiB  
Article
Dimension Reduction and Classifier-Based Feature Selection for Oversampled Gene Expression Data and Cancer Classification
by Olutomilayo Olayemi Petinrin, Faisal Saeed, Naomie Salim, Muhammad Toseef, Zhe Liu and Ibukun Omotayo Muyide
Processes 2023, 11(7), 1940; https://doi.org/10.3390/pr11071940 - 27 Jun 2023
Cited by 1 | Viewed by 1242
Abstract
Gene expression data are usually known for having a large number of features. Usually, some of these features are irrelevant and redundant. However, in some cases, all features, despite being numerous, show high importance and contribute to the data analysis. In a similar [...] Read more.
Gene expression data are usually known for having a large number of features. Usually, some of these features are irrelevant and redundant. However, in some cases, all features, despite being numerous, show high importance and contribute to the data analysis. In a similar fashion, gene expression data sometimes have limited instances with a high rate of imbalance among the classes. This can limit the exposure of a classification model to instances of different categories, thereby influencing the performance of the model. In this study, we proposed a cancer detection approach that utilized data preprocessing techniques such as oversampling, feature selection, and classification models. The study used SVMSMOTE for the oversampling of the six examined datasets. Further, we examined different techniques for feature selection using dimension reduction methods and classifier-based feature ranking and selection. We trained six machine learning algorithms, using repeated 5-fold cross-validation on different microarray datasets. The performance of the algorithms differed based on the data and feature reduction technique used. Full article
(This article belongs to the Special Issue Artificial Intelligence and Big Data for Healthcare and Biological Data Processing)
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Review

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24 pages, 3384 KiB  
Review
Critical Analysis of Risk Factors and Machine-Learning-Based Gastric Cancer Risk Prediction Models: A Systematic Review
by Zeyu Fan, Ziju He, Wenjun Miao and Rongrong Huang
Processes 2023, 11(8), 2324; https://doi.org/10.3390/pr11082324 - 02 Aug 2023
Viewed by 1276
Abstract
The gastric cancer risk prediction model used for large-scale gastric cancer screening and individual risk stratification is an artificial intelligence tool that combines clinical diagnostic data with a classification algorithm. The ability to automatically make a quantitative assessment of complex clinical data contributes [...] Read more.
The gastric cancer risk prediction model used for large-scale gastric cancer screening and individual risk stratification is an artificial intelligence tool that combines clinical diagnostic data with a classification algorithm. The ability to automatically make a quantitative assessment of complex clinical data contributes to increased accuracy for diagnosis with higher efficiency, significantly reducing the incidence of advanced gastric cancer. Previous studies have explored the predictive performance of gastric cancer risk prediction models, as well as the predictive factors and algorithms between each model, but have reached controversial conclusions. Thus, the performance of current machine-learning-based gastric cancer risk prediction models alongside the clinical relevance of different predictive factors needs to be evaluated to help build more efficient and feasible models in the future. In this systematic review, we summarize the current research progress related to the gastric cancer risk prediction model; discuss the predictive factors and methods used to construct the model; analyze the role of important predictive factors in gastric cancer, the preference of the selected classification algorithm, and the emphasis of evaluation criteria; and provide suggestions for the subsequent construction and improvement of the gastric cancer risk prediction model. Finally, we propose an improved approach based on the ethical issues of artificial intelligence in medicine to realize the clinical application of the gastric cancer risk prediction model in the future. Full article
(This article belongs to the Special Issue Artificial Intelligence and Big Data for Healthcare and Biological Data Processing)
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