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Applied Sciences
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Predictive Analytics in Healthcare
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Predictive Analytics in Healthcare

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".

Deadline for manuscript submissions: 20 May 2024 | Viewed by 6932

Special Issue Editor

Dr. Rasa Ruseckaite

E-Mail Website
Guest Editor
Public Health and Preventive Medicine, Monash University, Level 3, 553 St Kilda Rd, Melbourne, VIC 3004, Australia
Interests: quality of life; PROMs; rare diseases; clinical quality registries; clinical trials; new drugs and treatments
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Predictive analytics aims to alert clinicians and caregivers of the likelihood of events and outcomes before they occur, helping them to prevent and cure health issues. It utilises various techniques including modelling, data mining, and statistics, as well as artificial intelligence to evaluate historical and real-time data and make predictions about the future. These predictions offer a unique opportunity to see into the future and identify future trends in patient care both at an individual level and at a cohort scale.

We are interested in articles that explore predictive analysis. Potential topics include, but are not limited to, the following:

  • Data mining techniques and machine learning in healthcare;
  • Benefits and significance of predictive analytics;
  • Challenges and opportunities of big data analytics;
  • Impact on care;
  • Legal and ethical considerations of predictive analytics in healthcare.

Dr. Rasa Ruseckaite
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
  • big data
  • prediction
  • machine learning
  • optimization
  • healthcare

Published Papers (3 papers)

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Research

23 pages, 1718 KiB  
Article
Evaluation of Synthetic Categorical Data Generation Techniques for Predicting Cardiovascular Diseases and Post-Hoc Interpretability of the Risk Factors
by Clara García-Vicente, David Chushig-Muzo, Inmaculada Mora-Jiménez, Himar Fabelo, Inger Torhild Gram, Maja-Lisa Løchen, Conceição Granja and Cristina Soguero-Ruiz
Appl. Sci. 2023, 13(7), 4119; https://doi.org/10.3390/app13074119 - 23 Mar 2023
Cited by 4 | Viewed by 2358
Abstract
Machine Learning (ML) methods have become important for enhancing the performance of decision-support predictive models. However, class imbalance is one of the main challenges for developing ML models, because it may bias the learning process and the model generalization ability. In this paper, [...] Read more.
Machine Learning (ML) methods have become important for enhancing the performance of decision-support predictive models. However, class imbalance is one of the main challenges for developing ML models, because it may bias the learning process and the model generalization ability. In this paper, we consider oversampling methods for generating synthetic categorical clinical data aiming to improve the predictive performance in ML models, and the identification of risk factors for cardiovascular diseases (CVDs). We performed a comparative study of several categorical synthetic data generation methods, including Synthetic Minority Oversampling Technique Nominal (SMOTEN), Tabular Variational Autoencoder (TVAE) and Conditional Tabular Generative Adversarial Networks (CTGANs). Then, we assessed the impact of combining oversampling strategies and linear and nonlinear supervised ML methods. Lastly, we conducted a post-hoc model interpretability based on the importance of the risk factors. Experimental results show the potential of GAN-based models for generating high-quality categorical synthetic data, yielding probability mass functions that are very close to those provided by real data, maintaining relevant insights, and contributing to increasing the predictive performance. The GAN-based model and a linear classifier outperform other oversampling techniques, improving the area under the curve by 2%. These results demonstrate the capability of synthetic data to help with both determining risk factors and building models for CVD prediction. Full article
(This article belongs to the Special Issue Predictive Analytics in Healthcare)
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12 pages, 1463 KiB  
Article
Application of Machine Learning Predictive Models for Early Detection of Glaucoma Using Real World Data
by Murugesan Raju, Krishna P. Shanmugam and Chi-Ren Shyu
Appl. Sci. 2023, 13(4), 2445; https://doi.org/10.3390/app13042445 - 14 Feb 2023
Cited by 3 | Viewed by 1598
Abstract
Early detection of glaucoma is critically important for the prevention of irreversible blindness. We developed a predictive analytic framework through temporal data carpentry and applications of a suite of machine learning and logistic regression methods for the early prediction of glaucoma using electronic [...] Read more.
Early detection of glaucoma is critically important for the prevention of irreversible blindness. We developed a predictive analytic framework through temporal data carpentry and applications of a suite of machine learning and logistic regression methods for the early prediction of glaucoma using electronic health records (EHR) from over 650 hospitals and clinics across the USA. Four different machine-learning classification methods were applied using the whole dataset for predictive analysis. The accuracy, sensitivity, specificity, and f1 score were calculated using five-fold cross-validation to train and refine the models. The XGBoost, multi-layer perceptron (MLP), and random forest (RF) performed comparably well based on the area under the receiver operating characteristics curve (AUC) score of 0.81 for predicting glaucoma one year before the onset of the disease compared to the logistic regression (LR) score of 0.73. This study suggests that the ML methods can capture potential pre-glaucoma patients in advance before the occurrence of clinical symptoms from their history of EHR encounters, thus possibly leading to earlier intervention and preventive treatment. Full article
(This article belongs to the Special Issue Predictive Analytics in Healthcare)
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28 pages, 4025 KiB  
Article
An Efficient Prediction System for Coronary Heart Disease Risk Using Selected Principal Components and Hyperparameter Optimization
by Karna Vishnu Vardhana Reddy, Irraivan Elamvazuthi, Azrina Abd Aziz, Sivajothi Paramasivam, Hui Na Chua and Satyamurthy Pranavanand
Appl. Sci. 2023, 13(1), 118; https://doi.org/10.3390/app13010118 - 22 Dec 2022
Cited by 8 | Viewed by 2067
Abstract
Medical science-related studies have reinforced that the prevalence of coronary heart disease which is associated with the heart and blood vessels has been the most significant cause of health loss and death globally. Recently, data mining and machine learning have been used to [...] Read more.
Medical science-related studies have reinforced that the prevalence of coronary heart disease which is associated with the heart and blood vessels has been the most significant cause of health loss and death globally. Recently, data mining and machine learning have been used to detect diseases based on the unique characteristics of a person. However, these techniques have often posed challenges due to the complexity in understanding the objective of the datasets, the existence of too many factors to analyze as well as lack of performance accuracy. This research work is of two-fold effort: firstly, feature extraction and selection. This entails extraction of the principal components, and consequently, the Correlation-based Feature Selection (CFS) method was applied to select the finest principal components of the combined (Cleveland and Statlog) heart dataset. Secondly, by applying datasets to three single and three ensemble classifiers, the best hyperparameters that reflect the pre-eminent predictive outcomes were investigated. The experimental result reveals that hyperparameter optimization has improved the accuracy of all the models. In the comparative studies, the proposed work outperformed related works with an accuracy of 97.91%, and an AUC of 0.996 by employing six optimal principal components selected from the CFS method and optimizing parameters of the Rotation Forest ensemble classifier. Full article
(This article belongs to the Special Issue Predictive Analytics in Healthcare)
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